Adjustment rule generating and control method and apparatus

ABSTRACT

An adjustment control method and apparatus of this invention assume that a dependency relationship table representing qualitative characteristics, in which manipulated variables are classified in units of change patterns of controlled variables, and are characterized in that an adjustment rule for adjustment is generated. An adjustment control method and apparatus of this invention is characterized in that it is determined whether the current object situation exhibits an exceptional behavior (vibration/saturation), on the basis of selection of an adjusted controlled variable and a manipulated variable obtained from an automatically generated adjustment rule and a past operation in response to an occasionally output deviation. If it is determined that no exceptional behavior is observed, the instruction of the generated adjustment rule is performed, otherwise, the correction amount of the. manipulated variable which is input to the object to be adjusted is given assuming that a predetermined input operation is performed.

BACKGROUND OF THE INVENTION

The present invention relates to an adjustment rule generating methodand apparatus for generating an adjustment rule for appropriately andeasily adjusting an input to a multiple-input/output system havingnonlinear characteristics to obtain a desired output from the system,and an adjustment control method and apparatus for adjusting the inputto the system using the generated adjustment rule.

In an adjustment operation at a plant, a device production line, or amaintenance operation, when a certain element in the system is adjusted,a plurality of other elements vary upon adjustment, so it is oftendifficult to properly adjust all elements.

How to adjust an input to obtain a desired output is a general problem.To solve this problem, various means have been implementedconventionally.

In fact, the problem of an adjustment parameter (to be simply referredto as a parameter hereinafter) and the output is often concomitant withthe original input/output relationship. For this reason, an effectiveresult can hardly be obtained.

As reasons for this, the following three main factors are considered.

1. Complex correlation between the parameter and the output

2. Nonlinearity of the parameter and output

3. Maldistribution of data

Both the parameter and the output are generally multidimensional ratherthan one-dimensional (variable) and have complex causality. Therelationship between the parameter and the output is not linear.Resultant data is small in quantity or maldistributed, so thecharacteristics between the parameter and the output cannot besufficiently described using such data. It can be supposed that thesefactors make the problem difficult to solve.

To solve this problem, not only means based on the theory of linearmathematics but also means reflecting the farsighted knowledge orintuition of persons who have been concerned in actual adjustment havebeen used. For example, a method using fuzzy inference or qualitativecausality reasoning is used.

The fuzzy inference can be effective for a system having nonlinearcharacteristics. However, the fuzzy inference is regarded eventually“successful” only when the membership function or adjustment rule can beappropriately defined.

Generally, the fuzzy theory is applied to a nonlinear system. However,analogical reasoning can hardly be made because the response from anobject is not linear. In addition, trial and error in systemidentification also tends to be cumbersome. Even when the system can beidentified using a nonlinear model, the input amount for adjustment(manipulated variable for control) is hard to calculate because of thenonlinear model. This results in a difficulty in setting the membershipfunction or adjustment rule. Furthermore, it cannot be guaranteed thatthe initial rule is still effective for a variation in systemcharacteristics.

Essentially, this also applies to qualitative causality reasoning. Oncethe causality is clarified, analysis is automatically performed by acomputer. However, data in checking the causality depends on humandetermination, like the fuzzy inference. More specifically, even whendata is to be semi-automatically processed and modeled, the human datadetermination reference must be defined in advance. In this respect, thequalitative causality reasoning is essentially identical to the fuzzyinference (e.g., Jpn. Pat. Appln. KOKAI Publication No. 7-191706).

In reasoning based on causality, normally, the current state is analyzedon the basis of past data (past events). This processing requires alarge quantity of past data. This method is convenient when a relativelylarge plant (system) is operated for a long time. However, when anindividual difference between objects is assumed as in adjustingparameters of individual products on a production line, or whenadjustment is to be made in response to an environmental change, thenumber of data is limited because adjustment cannot always depend onother individual data. Therefore, adjustment can hardly be performedusing the method based on the conventional event data.

Reasoning does not suffice for adjustment. Unlike system observationbased on two references, e.g., faulty diagnosis for checking whether theinterior of a system is faulty or not (subsequent processing is left tohuman operations), some action must be taken for the system aftersituation determination in the control system.

BRIEF SUMMARY OF THE INVENTION

The present invention has been made in consideration of the abovesituation, and has as its object to provide a system having thefollowing characteristic features.

1. Adjustment is performed while sampling data

2. A large quantity of data is not required in advance

3. Nonlinear characteristics can be coped with.

More specifically, the present invention has as its object to provide anadjustment rule generating method and apparatus for generating anadjustment rule to adjust an object having multiple variables(multiple-input/output system) whose correlation has complex nonlinearcharacteristics.

It is another object of the present invention to provide an adjustmentcontrol method and apparatus for adjusting an object in accordance witha generated adjustment rule.

According to the present invention, the adjustment operation can beappropriately standardized and automated.

The adjustment rule generating method and apparatus of the presentinvention are characterized in that a table (dependency relationshiptable) representing qualitative characteristics is assumed in whichinputs (to be referred to as manipulated variables hereinafter) areclassified in units of change patterns of outputs (to be referred to ascontrolled variables hereinafter) having influence, and

an operation procedure (to be referred to as an adjustment rulehereinafter) for adjustment is generated.

The adjustment control method and apparatus according to the presentinvention are characterized in that it is determined whether the currentobject situation exhibits an exceptional behavior(vibration/saturation), on the basis of an instruction (selection of anadjusted controlled variable and a manipulated variable) obtained froman automatically generated adjustment rule and a past operation inresponse to an occasionally output deviation. If it is determined thatno exceptional behavior is observed, the instruction of the generatedadjustment rule is executed; otherwise, the correction amount of themanipulated variable which is input to the object to be adjusted isgiven assuming that a predetermined input operation is performed.

(1) An adjustment rule generating apparatus which determines themanipulated variable of the adjustment object or sets the value of avariable parameter (the variable parameter will not particularly bediscriminated from the manipulated variable hereinafter) of anadjustment object such that a controlled variable within an allowablerange can be obtained, is characterized by comprising

adjustable controlled variable selection means for receiving a change incontrolled variable corresponding to each manipulated variable of theadjustment object and qualitative feature data of a change differencebetween controlled variables and defining some manipulated variableswhich can be independently adjusted from the feature data in units ofcontrolled variables, and adjustment rule format generating means forconverting adjustable controlled variable data output from theadjustable controlled variable selection means in units of manipulatedvariables into a predetermined format and outputting the format as anadjustment procedure.

(2) The adjustment rule generating apparatus of arrangement (1) ischaracterized in that the change in controlled variable corresponding toeach manipulated variable of the adjustment object is defined by inputdata (manipulated variable characteristics and input/output dependencyrelationship table; to be referred to as a dependency table hereinafter)as binary data which describes whether each manipulated variable affectsthe controlled variable and binary data of a change pattern given by themanipulated variable to the controlled variable and expressing thequalitative feature data of the change difference between controlledvariables.

(3) An adjustment control apparatus for performing a proportionaloperation is characterized by comprising

deviation data generating means for calculating a deviation of acontrolled variable of an adjustment object and outputting thedeviation, adjustment rule storage means for receiving the controlledvariable deviation obtained from the deviation data generating means andstoring an adjustment rule obtained by the apparatus of arrangement (1)or (2), application rule selection means for receiving the controlledvariable deviation calculated by the deviation data generating means andthe adjustment rule stored in the adjustment rule storage means,selecting a manipulated variable to be adjusted, and defining theselected manipulated variable as an application rule, and manipulatedvariable determination means for determining a correction amount of themanipulated variable selected by the application rule selection meanswith reference to the controlled variable deviation as a predeterminedproportional amount of the deviation of the controlled variablecorresponding to the manipulated variable defined by the applicationrule.

(4) The adjustment control apparatus of arrangement (3) which performs aproportional operation and nonlinear avoidance is characterized byfurther comprising adjustment history data storage means forrecording/updating adjustment history data (adjustment count,manipulated variable, controlled variable deviation, and the like), andin that

the manipulated variable determination means refers to the controlledvariable deviation and the adjustment history data stored in theadjustment history data storage means in accordance with the applicationrule selected by the application rule selection means to determine thecorrection amount of the manipulated variable of the application rule ora manipulated variable other than the manipulated variable as aproportional amount of the controlled variable deviation or a relativedifference from another controlled variable deviation, or independentlyof the proportional amount, newly stores the determined manipulatedvariable or controlled variable deviation data referred to indetermining the manipulated variable in the adjustment history datastorage means, and updates the adjustment history data.

(5) The adjustment control apparatus of arrangement (4) which performs atest operation and nonlinear avoidance is characterized in that

the application rule determination means also discriminates between testadjustment and actual adjustment for identifying characteristics of theobject with reference to the adjustment history data stored in theadjustment history data storage means, and

the manipulated variable determination means refers to data obtainedfrom the adjustment history data storage means and the currentcontrolled variable deviation of the adjustment object to determine amanipulated variable for test adjustment or actual adjustment, newlystores the determined manipulated variable or controlled variabledeviation data referred to in determining the manipulated variable inthe adjustment history data storage means, and updates the adjustmenthistory data.

(6) An adjustment possibility evaluation apparatus is characterized bycomprising

an input unit for inputting the adjustment rule obtained from theadjustment rule generating apparatus of arrangement (1) or (2),

rule candidate initial setting means for generating some adjustmentrules in which manipulated variables and controlled variables are inone-to-one correspondence,

controlled variable selection means for selecting a controlled variableto check whether adjustment is enabled for each candidate rule set bythe rule candidate initial setting means,

corresponding manipulated variable search means for searching for amanipulated variable which corresponds to the controlled variableselected by the controlled variable selection means and can adjust thecontrolled variable,

rule candidate generating means for storing the candidate rule as a rulecandidate when all the controlled variables can be adjusted on the basisof the candidate rule set by the rule candidate initial setting means,and

rule group generating means for outputting a rule group while omittingthe same rule candidate stored in the rule candidate generating means.

(7) An adjustment rule candidate generating apparatus for preparing adependency table and an adjustment rule is characterized by comprising

dependency table candidate generating means for generating somedependency table candidates defined in arrangement (3) from actualinput/output data of the adjustment object, adjustment rule generatingmeans of arrangement (3), which receives each dependency table candidateto acquire an adjustment rule corresponding to the dependency tablecandidate, and dependency table/rule candidate storage means for storingthe adjustment rule obtained from the adjustment rule generatingapparatus in correspondence with the dependency table candidate.

In the adjustment rule generating apparatus of arrangement (1),

the change in controlled variable corresponding to each manipulatedvariable of the adjustment object and qualitative feature data of achange difference between controlled variables are input to theadjustable controlled variable selection means,

the adjustable controlled variable selection means defines somemanipulated variables which can be independently adjusted in units ofcontrolled variables from the received feature data and outputsadjustable controlled variable data representing the relationshipbetween the manipulated variable and the controlled variable, and

the adjustment rule format generating means converts the adjustablecontrolled variable data output from the adjustable controlled variableselection means in units of manipulated variables into a predeterminedformat and outputs adjustment procedure data (adjustment rule).

In the arrangement (2), the change in controlled variable correspondingto each manipulated variable of the adjustment object is defined byinput data (manipulated variable characteristics and input/outputdependency relationship table; to be referred to as a dependency tablehereinafter) as binary data which describes whether each manipulatedvariable affects the controlled variable and binary data of a changepattern given by the manipulated variable to the controlled variable andexpressing the qualitative feature data of the change difference betweencontrolled variables,

the adjustable controlled variable selection means defines somemanipulated variables which can be independently adjusted in units ofcontrolled variables from the received feature data and outputsadjustable controlled variable data representing the relationshipbetween the manipulated variable and the controlled variable, and

the adjustment rule format generating means converts the adjustablecontrolled variable data output from the adjustable controlled variableselection means in units of manipulated variables into a predeterminedformat and outputs adjustment procedure data (adjustment rule).

In the adjustment control apparatus of arrangement (3) which performs aproportional operation,

the deviation data generating means calculates a deviation of acontrolled variable of an adjustment object,

the adjustment rule storage means stores an adjustment rule obtained bythe adjustment rule generating apparatus of arrangement (1) or (2),

the application rule selection means receives the controlled variabledeviation and the adjustment rule stored in the adjustment rule storagemeans, selects a manipulated variable to be adjusted, and outputs it asan application rule, and

the manipulated variable determination means determines a correctionamount of the manipulated variable selected by the application ruleselection means with reference to the controlled variable deviation as apredetermined proportional amount of the deviation of the controlledvariable corresponding to the manipulated variable defined by theapplication rule.

In the adjustment control apparatus of arrangement (4) which performs aproportional operation and nonlinear avoidance,

the deviation data generating means calculates a deviation of acontrolled variable of the adjustment object,

the adjustment history data storage means records/updates adjustmenthistory data (adjustment count, manipulated variable, controlledvariable deviation, and the like),

the application rule selection means determines an application rule fromthe adjustment rules for adjustment, and

the manipulated variable determination means refers to the controlledvariable deviation and the adjustment history data stored in theadjustment history data storage means to determine the correction amountof the manipulated variable according to the application rule or amanipulated variable other than the manipulated variable as aproportional amount of the controlled variable deviation or a relativedifference from another controlled variable deviation, or independentlyof the proportional amount, newly stores the determined manipulatedvariable or controlled variable deviation data referred to indetermining the manipulated variable in the adjustment history datastorage means, and updates the adjustment history data.

In the adjustment control apparatus of arrangement (5) which performs atest operation and nonlinear avoidance,

the deviation data generating means calculates a deviation of acontrolled variable of the adjustment object,

the application rule determination means discriminates between testadjustment and actual adjustment for identifying characteristics of theobject with reference to the adjustment history data stored in theadjustment history data storage means, and

the manipulated variable determination means refers to data obtainedfrom the adjustment history data storage means and the currentcontrolled variable deviation of the adjustment object to determine amanipulated variable for test adjustment or actual adjustment, newlystores the determined manipulated variable or controlled variabledeviation data referred to in determining the manipulated variable inthe adjustment history data storage means, and updates the adjustmenthistory data.

In the adjustment possibility evaluation apparatus of arrangement (6),

the adjustment rule obtained from the adjustment rule generatingapparatus of arrangement (1) or (2) is obtained as an input,

the rule candidate initial setting means generates some adjustment rulesin which manipulated variables and controlled variables are inone-to-one correspondence,

the controlled variable selection means selects a controlled variable tocheck whether adjustment is enabled for each candidate rule set by therule candidate initial setting means,

the corresponding manipulated variable search means searches for amanipulated variable which corresponds to the controlled variableselected by the controlled variable selection means and can adjust thecontrolled variable,

the rule candidate generating means stores the candidate rule as a rulecandidate when all the controlled variables can be adjusted on the basisof the candidate rule set by the rule candidate initial setting means,and

the rule group generating means outputs a rule group while omitting thesame rule candidate stored in the rule candidate generating means.

In the adjustment rule candidate generating apparatus of arrangement (7)which prepares a dependency table and an adjustment rule,

the dependency table candidate generating means generates somedependency table candidates defined in arrangement (3) from actualinput/output data of the adjustment object, the adjustment rulegenerating apparatus of arrangement (3) receives each dependency tablecandidate and generates an adjustment rule corresponding to thedependency table candidate, and the dependency table/rule candidatestorage means stores the adjustment rule obtained from the adjustmentrule generating apparatus in correspondence with the dependency tablecandidate.

According to the present invention, there is also provided an adjustmentrule generating apparatus which determines a second data group such thata first data group corresponding to a predetermined object has a desiredvalue, characterized by comprising

adjustable controlled variable selection means for obtaining a changepattern in units of outputs of controlled variables affected by themanipulated variable using actual data of the object, receiving, as aninput, feature data representing qualitative characteristics classifiedin accordance with the change pattern, and defining some manipulatedvariables which can adjust one or more controlled variables includingthe controlled variable, in units of controlled variables, on the basisof the feature data and the influence of the manipulated variable andthe controlled variable, and

adjustment rule format generating means for converting the adjustablecontrolled variable data output from the adjustable controlled variableselection means into a predetermined format in units of manipulatedvariables on the basis of the feature data and the influence of themanipulated variable and the controlled variable and outputting theformat as an adjustment procedure.

There is also provided an adjustment rule generating apparatus whichdetermines a second data group such that a first data groupcorresponding to a predetermined object has a desired value,characterized by comprising

adjustable controlled variable selection means for receiving a change incontrolled variable corresponding to each manipulated variable of theobject and qualitative feature data of a change difference betweencontrolled variables and defining one or more manipulated variableswhich can adjust one or more controlled variables including thecontrolled variable, in units of controlled variables, on the basis ofthe feature data and the influence of the manipulated variable and thecontrolled variable, and

adjustment rule format generating means for converting the adjustablecontrolled variable data output from the adjustable controlled variableselection means into a predetermined format in units of manipulatedvariables on the basis of the feature data and the influence of themanipulated variable and the controlled variable and outputting theformat as an adjustment procedure.

The change in controlled variable corresponding to each manipulatedvariable of the adjustment. object is defined by input data as binarydata which describes whether each manipulated variable affects thecontrolled variable and binary data of a change pattern given by themanipulated variable to the controlled variable and expressing thequalitative feature data of the change difference between controlledvariables.

An adjustment possibility evaluation apparatus comprises

rule candidate initial setting means for receiving an adjustment ruleobtained from one of the adjustment rule generating apparatuses andgenerating some adjustment rules in which manipulated variables andcontrolled variables are in one-to-one correspondence,

controlled variable selection means for selecting a controlled variableto check whether adjustment is enabled for each candidate rule set bythe rule candidate initial setting means,

corresponding manipulated variable search means for searching for amanipulated variable which corresponds to the controlled variableselected by the controlled variable selection means and capable ofadjusting the controlled variable,

rule candidate generating means for storing the candidate rule as a rulecandidate when all the controlled variables can be adjusted on the basisof the candidate rule set by the rule candidate initial setting means,and

rule group generating means for outputting a rule group while omittingthe same rule candidate stored in the rule candidate generating means.

There is also provided an adjustment rule generating method ofdetermining a second data group such that a first data groupcorresponding to a predetermined object has a desired value,characterized by comprising

on the basis of data obtained on the basis of the object, obtainingpredetermined first data affected by predetermined second data andchange characteristics between the predetermined first data,

selecting specific one of the first data, which has changecharacteristics between outputs capable of correcting the changecharacteristics between first data, and

determining specific second data which can correspond to the selectedspecific first data from the second data group.

According to the present invention, there is also provided an adjustmentrule generating method of determining a second data group such that afirst data group corresponding to a predetermined object has a desiredvalue, characterized by comprising

receiving a change in controlled variable corresponding to eachmanipulated variable of the object and qualitative feature data of achange difference between controlled variables and defining one or moremanipulated variables which can adjust one or more controlled variablesincluding the controlled variable, in units of controlled variables, onthe basis of the feature data and the influence of the manipulatedvariable and the controlled variable,

converting the obtained adjustable controlled variable data into apredetermined format in units of manipulated variables on the basis ofthe feature data and the influence of the manipulated variable and thecontrolled variable and outputting the format as an adjustmentprocedure,

calculating a deviation of the controlled variable of the object andoutputting the deviation,

receiving a controlled variable deviation obtained from the output andstoring an obtained adjustment rule,

receiving the calculated controlled variable deviation and the storedadjustment rule, selecting a manipulated variable to be adjusted, anddefining the manipulated variable as an application rule, and

determining a correction amount of the manipulated variable selected bythe application rule selection unit with reference to the controlledvariable deviation as a predetermined proportional amount of thedeviation of the controlled variable corresponding to the manipulatedvariable defined by the application rule.

Additional objects and advantages of the invention will be set forth inthe description which follows, and in part will be obvious from thedescription, or may be learned by practice of the invention. The objectsand advantages of the invention may be realized and obtained by means ofthe instrumentalities and combinations particularly pointed out in theappended claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate presently preferred embodiments ofthe invention, and together with the general description given above andthe detailed description of the preferred embodiments give below, serveto explain the principles of the invention.

FIG. 1 is a view schematically showing the relationship between an inputand an output in the present invention;

FIG. 2 is a block diagram showing the schematic arrangement of anadjustment rule generating apparatus (first and second embodiments)according to the first and second embodiments of the present invention;

FIG. 3 is a flow chart showing the flow of processing in an adjustablecontrolled variable selection unit according to the second embodiment ofthe present invention;

FIG. 4 is the first partial enlarged view of the flow chart in FIG. 3;

FIG. 5 is the second partial enlarged view of the flow chart in FIG. 3;

FIG. 6 is the third partial enlarged view of the flow chart in FIG. 3;

FIG. 7 is the fourth partial enlarged view of the flow chart in FIG. 3;

FIG. 8 is the fifth partial enlarged view of the flow chart in FIG. 3;

FIG. 9 is a flow chart showing the flow of processing of an adjustmentrule format generating unit in the adjustment rule generating apparatus(second embodiment);

FIG. 10 is a block diagram showing the schematic arrangement of anadjustment control apparatus of the present invention;

FIG. 11 is a block diagram showing the schematic arrangement of anadjustment control apparatus according to the third embodiment of thepresent invention;

FIG. 12 is a view showing the chart 1 according to the third and fourthembodiment of the present invention;

FIG. 13 is a block diagram showing the schematic arrangement of anadjustment control apparatus according to the fourth embodiment of thepresent invention;

FIG. 14 is a block diagram showing the schematic arrangement of anadjustment control apparatus according to the fifth embodiment of thepresent invention;

FIG. 15 is a view showing the chart 2 according to the fifth embodimentof the present invention;

FIG. 16 is a block diagram showing the schematic arrangement of anadjustment possibility evaluation unit according to the sixth embodimentof the present invention;

FIG. 17 is a block diagram showing the schematic arrangement of anautomatic adjustment rule candidate generating apparatus according tothe seventh embodiment of the present invention;

FIG. 18A is a graph showing the change pattern of two outputs;

FIG. 18B is a graph showing the change pattern of two outputs;

FIG. 18C is a graph showing the change pattern of two outputs;

FIG. 19A is a graph showing the relationship between values ofparameters and the adjustment counts;

FIG. 19B is a graph showing the relationship between values ofparameters and the adjustment counts; and

FIG. 19C is a graph showing the relationship between values ofparameters and the adjustment counts.

DETAILED DESCRIPTION OF THE INVENTION

The embodiments of the present invention will be described below withreference to the accompanying drawing.

In the present invention, an input (manipulated variable) for eachelement is given to an object to be adjusted or controlled, and anoutput as the operation result of the object in response to the giveninput is obtained in units of elements. The change pattern of, e.g., twooutputs given by the input (manipulated variable) is checked.

More specifically, it is checked whether the two outputs change in thedirection of same sign (offset change: FIG. 18A) or change in directionsof different signs (gradient change; a change with gradient: FIGS. 18Band 18C).

In addition, the dependency relationship between various inputs andoutputs of the object to be adjusted or controlled is analyzed andrepresented as a table, and information of the change pattern is addedto this table.

Two inputs are selected from the table representing the dependencyrelationship, and the degree of influence between the input and outputis detected on the basis of the change pattern. With this process, theelement for which the input must be changed to perform most stablecontrol or adjustment as desired is checked.

FIG. 1 is a view schematically showing the relationship between an inputand an output.

The adjustment rule generating apparatus of the present inventiongenerates an operation procedure (adjustment rule) for adjustment on anassumption that actual object data, i.e., inputs xi and outputs yi (i isan integer) are obtained, and these actual data are used to prepare atable (dependency relationship table) representing the qualitativecharacteristics obtained by classifying inputs (manipulated variables)in units of change patterns of outputs (controlled variables) havinginfluences, as shown in FIG. 1.

In the adjustment control apparatus of the present invention, it isdetermined whether the current object situation exhibits an exceptionalbehavior (vibration/saturation), on the basis of an instruction(selection of an adjusted controlled variable and a manipulatedvariable) obtained from an automatically generated adjustment rule and apast operation in response to an occasionally output deviation. If it isdetermined that no exceptional behavior is observed, the instruction ofthe generated adjustment rule is executed; otherwise, the correctionamount of the manipulated variable which is input to the object to beadjusted is given assuming that a predetermined input operation isperformed.

An adjustment rule generating apparatus according to the firstembodiment of the present invention, which determines the manipulatedvariable of the adjustment object or sets the value of a variableparameter (the variable parameter will not particularly be discriminatedfrom the manipulated variable hereinafter) of an adjustment object suchthat a controlled variable within an allowable range can be obtained, ischaracterized by comprising

adjustable controlled variable selection means for receiving a change incontrolled variable corresponding to each manipulated variable of theadjustment object and qualitative feature data of a change differencebetween controlled variables and defining some manipulated variableswhich can be independently adjusted from the feature data in units ofcontrolled variables, and adjustment rule format generating means forconverting adjustable controlled variable data output from theadjustable controlled variable selection means in units of manipulatedvariables into a predetermined format and outputting the format as anadjustment procedure.

In the second embodiment, the adjustment rule generating apparatus ischaracterized in that feature data representing the change in controlledvariable caused by each manipulated variable of the adjustment object isexpressed as first binary data describing whether each manipulatedvariable affects each controlled variable, and the qualitative featuredata in the change in controlled variable between controlled variablesis expressed as second binary data describing change pattern given fromeach manipulated variable to each controlled variable, and these firstand second binary data is used as the input data(manipulated variablecharacteristics and input/output dependency relationship table; to bereferred to as a dependency table hereinafter).

The third embodiment relates to an adjustment control apparatus forperforming a proportional operation. This apparatus is characterized bycomprising

deviation data generating means for calculating a deviation of acontrolled variable of an adjustment object and outputting thedeviation, adjustment rule storage means for receiving the controlledvariable deviation obtained from the deviation data generating means andstoring an adjustment rule obtained in the first or second embodiment,application rule selection means for receiving the controlled variabledeviation calculated by the deviation data generating means and theadjustment rule stored in the adjustment rule storage means, selecting amanipulated variable to be adjusted, and defining the selectedmanipulated variable as an application rule, and manipulated variabledetermination means for determining a correction amount of themanipulated variable selected by the application rule selection meanswith reference to the controlled variable deviation as a predeterminedproportional amount of the deviation of the controlled variablecorresponding to the manipulated variable defined by the applicationrule.

The fourth embodiment relates to an adjustment control apparatus forperforming a proportional operation and nonlinear avoidance. In thefourth embodiment, the adjustment control apparatus of the thirdembodiment is characterized by further comprising adjustment historydata storage means for recording/updating adjustment history data(adjustment count, manipulated variable, controlled variable deviation,and the like), and in that

the manipulated variable determination means refers to the controlledvariable deviation and the adjustment history data stored in theadjustment history data storage means in accordance with the applicationrule selected by the application rule selection means to determine thecorrection amount of the manipulated variable of the application rule ora manipulated variable other than the manipulated variable as aproportional amount of the controlled variable deviation or a relativedifference from another controlled variable deviation, or independentlyof the proportional amount, newly stores the determined manipulatedvariable or controlled variable deviation data referred to indetermining the manipulated variable in the adjustment history datastorage means, and updates the adjustment history data.

The fifth embodiment relates to an adjustment control apparatus forperforming a test operation and nonlinear avoidance. In the fifthembodiment, the adjustment control apparatus of the fourth embodiment ischaracterized in that

the application rule determination means also discriminates between testadjustment and actual adjustment for identifying characteristics of theobject with reference to the adjustment history data stored in theadjustment history data storage means, and

the manipulated variable determination means refers to data obtainedfrom the adjustment history data storage means and the currentcontrolled variable deviation of the adjustment object to determine amanipulated variable for test adjustment or actual adjustment, newlystores the determined manipulated variable or controlled variabledeviation data referred to in determining the manipulated variable inthe adjustment history data storage means, and updates the adjustmenthistory data.

The sixth embodiment relates to an adjustment possibility evaluationapparatus. In the sixth embodiment, an adjustment possibility evaluationapparatus is characterized by comprising an input unit capable ofreceiving feature data of qualitative input/output relationship of theadjustment object or a dependency table and the adjustment rule obtainedfrom the adjustment rule generating apparatus of the first or secondembodiment, rule candidate initial setting means for generating someadjustment rules in which manipulated variables and controlled variablesare in one-to-one correspondence, controlled variable selection meansfor selecting a controlled variable to check whether adjustment isenabled for each candidate rule set by the rule candidate initialsetting means, corresponding manipulated variable search means forsearching for a manipulated variable which corresponds to the controlledvariable selected by the controlled variable selection means and canadjust the controlled variable, rule candidate generating means forstoring the candidate rule as a rule candidate when all the controlledvariables can be adjusted on the basis of the candidate rule set by therule candidate initial setting means, and rule group generating meansfor outputting a rule group while omitting the same rule candidatestored in the rule candidate generating means.

The seventh embodiment relates to an adjustment rule candidategenerating apparatus for preparing a dependency table and an adjustmentrule. In the seventh embodiment, an adjustment rule candidate generatingapparatus is characterized by comprising dependency table candidategenerating means for generating some dependency table candidates definedin the third embodiment from actual input/output data of the adjustmentobject, adjustment rule generating apparatus of the third embodiment,which receives each dependency table candidate to acquire an adjustmentrule corresponding to the dependency table candidate, and dependencytable/rule candidate storage means for storing the adjustment ruleobtained from the adjustment rule generating apparatus in correspondencewith the dependency table candidate.

A system shown in FIG. 10 comprises an adjustment rule generatingapparatus 10, an adjustment control apparatus 20, and an adjustmentobject 30. The block diagram of FIG. 10 shows a system arrangement-inwhich the adjustment control apparatus 20 outputs an instruction foradjusting the variable parameter of the adjustment object 30 on thebasis of an adjustment rule generated by the adjustment rule generatingapparatus 10 such that a desired output is obtained from the adjustmentobject 30.

The block diagram shown in FIG. 10 is associated with the first to fifthembodiments. The adjustment rule generating apparatus 10 or theadjustment control apparatus 20 will be described below in units ofembodiments.

(First Embodiment)

FIG. 2 is a block diagram showing the schematic arrangement of anadjustment rule generating apparatus 10 according to the firstembodiment and the second embodiment (to be described later). As shownin FIG. 2, the adjustment rule generating apparatus 10 comprises anadjustable controlled variable selection unit 11 and an adjustment ruleformat generating unit 12.

The adjustable controlled variable selection unit 11 selects anadjustable parameter (controlled variable= output from the adjustmentobject) to be adjusted and an adjustment element (manipulated variable=input to the adjustment object) used to adjust the parameter in anadjustment object 30.

The adjustment rule format generating unit 12 outputs an adjustment rulehaving a predetermined format on the basis of the combination of themanipulated variable and controlled variable selected by the adjustablecontrolled variable selection unit 11.

Tables 1 and 2 show examples of input data to the adjustable controlledvariable selection unit 11 according to the first embodiment. In thiscase, an adjustment object having three-dimensional inputs/outputs isexemplified. Table 1 shows dependency characteristic data, and Table 2shows controlled variable correlation characteristic data.

TABLE1 DEPENDENCY CHARACTERISTIC DATA (QUALITATIVE INPUT/OUTPUTDEPENDENCY CHARACTERISTICS OF ADJUSTMENT OBJECT) Manipulated VARIABLECONTROLLED (INPUT) VARIABLE (OUTPUT) X1 X2 X3 Y1 x ◯ x Y2 ◯ x ◯ Y3 ◯ x ◯NOTE) ◯X REPRESENTS PRESENCE/ABSENCE OF DEPENDENCY OF INPUT/OUTPUT ◯:DEPENDENCY IS PRESENT X: DEPENDENCY IS NOT PRESENT

TABLE 2 CONTROLLED VARIABLE CORRELATION CHARACTERISTIC DATA (QUALITATIVECHARACTERISTICS OF CHANGE DIFFERENCE BETWEEN CONTROLLED VARIABLES OFADJUSTMENT OBJECT) BETWEEN MANIPULATED CONTROLLED VARIABLE VARIABLES X1X2 X3 Y1 ⇄ Y2 C C C Y2 ⇄ Y3 A C B Y3 ⇄ Y1 C C C NOTE) A TO C REPRESENTCHANGES IN CHARACTERISTICS A: CHANGE IN SAME DIRECTION B: CHANGE INDIFFERENT DIRECTIONS C: ONLY ONE VARIABLE CHANGES

The dependency characteristic data (Table 1) is the qualitativeinput/output dependency characteristics of the adjustment object andindicates whether the manipulated variable and the controlled variable(input and output) of the adjustment object have a dependencyrelationship. A specific manipulated variable and a controlled variableadjusted by the specific manipulated variable can be determined from thetable of the dependency characteristic data. In Table 1, the followingrelationship is estimated.

Controlled variable Y1 is adjusted by manipulated variable X2

(1) Controlled variable Y2 or Y3 is adjusted by manipulated variable X1or X3

The controlled variable correlation characteristic data qualitativelyrepresents the change in controlled variable in response to eachmanipulated variable (relative change in characteristics between acontrolled variable and another controlled variable). In Table 2, thecharacteristics between two arbitrary controlled variables areclassified into three types (the number and characteristics inclassification can be arbitrarily defined).

A: “The two controlled variables change in the same direction”

B: “The two controlled variables change in different directions”

C: “Only one controlled variable changes” The relationship between thecontrolled variable of interest and the manipulated variable is limitedunder condition (1). When the relationships shown in Table 2 arereferred to for only that portion, adjustment characteristics below areestimated.

Manipulated variable X1< Change A in controlled variables Y2 and Y3

Manipulated variable X2→ Change C in controlled variable Y1(independent)

(2) Manipulated variable X3→ Change B in controlled variables Y2 and Y3

The adjustable controlled variable selection unit 11 outputs data whichrepresents the relationship between the controlled variable of theadjustment object and the manipulated variable for adjusting thecontrolled variable as adjustment characteristic data.

Table 3 shows an example of adjustment characteristic data.

TABLE 3 ADJUSTMENT CHARACTERISTIC DATA X1 Y2, Y3 A X2 Y1 C X3 Y2, Y3 B

More specifically, the adjustable controlled variable selection unit 11outputs the relationship “manipulated variable X1→ change A incontrolled variables Y2 and Y3” as adjustment characteristic data “X1Y2, Y3 A”, the relationship “manipulated variable X2→ change C incontrolled variable Y1 (independent)” as adjustment characteristic data“X2 Y1 C”, and the relationship “manipulated variable X3→ change B incontrolled variables Y2 and Y3” as adjustment characteristic data “X3Y2, Y3 B”.

The adjustment rule format generating unit 12 converts the adjustmentcharacteristic data output from the adjustable controlled variableselection unit 11 into a specific format of adjustment rules withreference to the characteristic data of the adjustment object shown inTables 1 and 2. The adjustment characteristic data having contents shownin Table 3 is interpreted in units of rows, and the relationship betweenthe controlled variable to be adjusted and the manipulated variable isdefined as an adjustment rule corresponding to the condition inaccordance with the controlled variable correlation characteristics (A,B, and C) defined in Table 2.

More specifically, as indicated by the adjustment characteristic data inTable 3, for the first row, it is determined that “since “Y2 and Y3 arechanged by X1 in the same direction (A)”, if Y2 and Y3 have almost thesame deviation and are out of the allowable deviation, Y2 and Y3 can beadjusted using X1 for changing the two deviations in the samedirection”. For the second row, it is determined that “since “Y1 changesdepending only on X2 (C)”, when Y1 is out of the allowable deviation, Y1can be adjusted using X2”. For the third row, it is determined “since“Y2 and Y3 are changed by X3 in different directions (B)”, if Y2 and Y3have different deviations and are out of the allowable deviation, Y2 andY3 can be adjusted using X3 for changing the two deviations in differentdirections”.

These are output as adjustment rules in the following format:$\begin{matrix}{\left( {ɛ_{1},ɛ_{2},{{and}\quad ɛ_{3}\quad {are}\quad {allowable}\quad {deviations}}} \right)\left\{ \begin{matrix}{\left( {{Y_{2}} > {ɛ_{2}\bigvee{Y_{3}}} > ɛ_{3}} \right)\bigwedge{\left( {Y_{2} \simeq Y_{3}} \right){TuneX}_{1}}} \\{{Y_{1}} > {ɛ_{1}{TuneX}_{2}}} \\{\left( {{Y_{2}} > {ɛ_{2}\bigvee{Y_{3}}} > ɛ_{3}} \right)\bigwedge{\left( {Y_{2} ≄ Y_{3}} \right){TuneX}_{3}}}\end{matrix} \right.} & (3)\end{matrix}$

Although Table 1 is not referred to in the above description, prioritymay be given to each rule using Table 1 in accordance with the number ofdependency relationships (number of 0s).

The rule of the above format (3) does not give the specific correctionamount of each manipulated variable. However, the correction amount canbe set using a conventionally well-known scheme as follows.

1. A correction amount is occasionally set by human determination.

2. A predetermined correction amount is set in accordance with the signof deviation.

3. A correction amount proportional to the sign/magnitude of deviationis set.

(Second Embodiment)

The second embodiment is characterized in that the adjustment featuredata in the first embodiment is classified into two types, and thearrangements and input/output data of an adjustable controlled variableselection unit 11 and an adjustment rule format generating unit 12 aremade to correspond to this classification.

Table 4 shows data to be input to the adjustable controlled variableselection unit 11.

TABLE 4 INPUT DATA “DEPENDENCY TABLE” 3 1 0 1 0 1 1 0 1 1 1 1 1

Table 4 is in case of an adjustment object with three inputs and threeoutputs. In Table 4, the controlled variable correlation characteristicdata and the adjustment feature data in the first embodiment are puttogether, and the respective data are represented as binary data (“3” inTable 4 is additional data representing the number of dimensions but notdirectly indicating the characteristic feature of the dependencyrelationship between an input and an output). Such a table will beparticularly referred to as a “dependency table” hereinafter.

The “dependency table” describes the following three data as shown inTable 5.

TABLE 5 DESCRIPTION OF “DEPENDENCY TABLE” DEPENDENCY TABLE DESCRIPTION 3NUMBER OF MANIPULATED VARIABLES 1 0 1 CHANGE PATTERN OF CONTROLLEDVARIABLES DUE TO EACH MANIPULATED VARIABLE (ADJUSTMENT CHARACTERISTICDATA: CHANGE IN THE SAME DIRECTION “0” OR CHANGE IN 0 1 1 DIFFERENTDIRECTIONS “1”) WHETHER EACH MANIPULATED VARIABLE AFFECTS THE FIRSTCONTROLLED VARIABLE (1) OR NOT (0) (CONTROLLED VARIABLE 0 1 1 CORRECTIONCHARACTERISTIC DATA) WHETHER EACH MANIPULATED VARIABLE AFFECTS THESECOND CONTROLLED VARIABLE (1) OR NOT (0) (CONTROLLED VARIABLE 1 1 1CORRELATION CHARACTERISTIC DATA) WHETHER EACH MANIPULATED VARIABLEAFFECTS THE THIRD CONTROLLED VARIABLE (1) OR NOT (0) (CONTROLLEDVARIABLE CORRELATION CHARACTERISTIC DATA)

Upon receiving the data in the dependency table shown in Table 4, theadjustable controlled variable selection unit 11 determines combinationsof manipulated variables and controlled variables by processingfollowing checklists 1 and 2 and a procedure shown in FIG. 2 on thebasis of the dependency table. With this procedure, the adjustablecontrolled variable selection unit 11 outputs the priority of eachmanipulated variable and a controlled variable which must beunconditionally adjusted by the manipulated variable as “rule data”.

Manipulated variables will be classified in accordance with the changepattern of corresponding controlled variables: “a manipulated variablefor changing controlled variables in the same direction (i.e., changepattern “10”)” will be called “an offset manipulated variable”; and “amanipulated variable for changing controlled variables in differentdirections (i.e., change pattern “1”)” will be called “a gradientmanipulated variable” hereinafter. In addition, the dependencyrelationship between a manipulated variable and a controlled variablewill be simply expressed as “correspondence”.

[Checklist 1]

I. For a square matrix in the dependency table where whether eachmanipulated variable affects a controlled variable is described, rowsums and column sums are calculated.

II. Priority is given to manipulated variables in descending order ofthe column sum values (if the values equal, priority is given to offsetmanipulated variables. If the values still equal, priority is set inaccordance with the manipulated variable number).

III. Priority is given to controlled variables in ascending order of therow sum values (control variables with the same value are allowed tohave the same priority).

[Checklist 2] (FIGS. 3 to 8)

Step I. A manipulated variable with low priority is selected (“selectedmanipulated variable”).

Step II. The manipulated variable is an offset manipulated variable(“selected offset manipulated variable”).

(II-i) One of controlled variables with high priority, for which nomanipulated variable is defined, is selected (“selected controlledvariable”).

(II-ii) The number of controlled variables employed as rules togetherwith the selected manipulated variable is “0” or “1” (smaller than “2”).

Step II-ii-1. Check 0: an offset manipulated variable other than theselected offset manipulated variable corresponds to the selectedcontrolled variable.

→ Check 0 flag: number (which is selected first) of the offsetmanipulated variable (other than the selected offset manipulatedvariable)

Step II-ii-2. Check 1: the controlled variable corresponds to theselected offset manipulated variable.

→ Check 1 flag: 1 <present>

(II-iii) The number of controlled variables employed as rules togetherwith the selected manipulated variable is “1”.

Step II-iii-1. Check 2: upon searching for manipulated variables withlow priority in ascending order, a gradient manipulated variable whichcorresponds to the selected controlled variable and is not employed as arule is present.

→ Check 2 flag: the number of the gradient manipulated variable (whichis selected first).

Step II-iii-2. Check 3: upon searching for manipulated variables withlow priority in ascending order, a gradient manipulated variable whichis not employed as a rule and corresponds to the selected controlledvariable for another offset manipulated variable is present.

→ Check 3 flag: number of the gradient manipulated variable (which isselected first).

(II-iv) The dependency relationship pattern is searched for (initialvalue: check level 0).

Step II-iv-1. When check level is “0”: when no controlled variable isselected.

→ Rule flag: contents of check 1 flag

Step II-iv-2. When check level is “1”: when no controlled variable isselected.

→ Rule flag: contents of check 1 flag

Step II-iv-3. When check level is “2”: when one controlled variable isselected.

→ Rule flag: contents of check 2 flag

Step II-iv-4. When check level is “3”: one controlled variable isselected.

→ Rule flag: contents of check 3 flag

Step II-iv-5. When check level is “14”: when one controlled variable isselected.

→ Rule flag: contents of check 2 flag

(II-v) Determination of combination (when the flag is different from theinitial value)

Step II-v-1. When one controlled variable is selected

→ Check level= 2

Step II-v-2. When two controlled variables are selected

→ Rule determination, check level= no change

Step II-v-3. Updating processing

→ Flag initialization

(II-vi) Special processing performed when two controlled variables areselected (a controlled variable corresponding to the offset manipulatedvariable is “primary”, and a controlled variable corresponding to thegradient manipulated variable is “secondary”).

Step II-vi-1. When check level is “3”, the primary and secondarycontrolled variable are exchanged.

Step II-vi-2. Indication that the primary and secondary controlledvariables cannot be determined.

Step III. A gradient manipulated variable which corresponds to only onecontrolled variable and is not selected in determining the combinationsof offset manipulated variables is selected.

Checklist 2 has the above-described arrangement.

The flow of processing in the adjustable controlled variable selectionunit 11 for Table 4 will be described below.

1. The row sums and column sums for the adjustment feature data portionof the dependency table are calculated in accordance with the [Checklist1].

Following Table 6 describes a result of the row sums and column sums inexample of the Table 4.

TABLE 6 THREE-DIMENSIONAL EXAMPLE IN TABLE 4 MANIPULATED VARIABLE ROW X1 X 2 X 3 SUM CONTROLLED 0 1 1 2 VARIABLE Y 1 CONTROLLED 0 1 1 2VARIABLE Y 2 CONTROLLED 1 1 1 3 VARIABLE Y 3 COLUMN SUM 1 3 3

2. The order of manipulated variables is defined in accordance with IIof [Checklist 1].

Following Table 7 describes the order of manipulated variables inexample of Table 4.

TABLE 7 ORDER OF MANIPULATED VARIABLE IN EXAMPLE OF TABLE 4 MANIPULATEDVARIABLE X 1 X 2 X 3 ORDER 3 1 2

3. The order of controlled variables is defined in accordance with IIIof [Checklist 1].

Following Table 8 describes the order of controlled variables in exampleof Table 4.

TABLE 8 ORDER OF CONTROLLED VARIABLES IN EXAMPLE OF TABLE 4 CONTROLLEDVARIABLE Y 1 Y 2 Y 3 ORDER 1 1 2

4. If there are certain manipulated variables of which changecharacteristics are correctable, the combination of them is determinedin accordance with [Checklist 2] (refer to FIGS. 3 to 8).

(4-1) The manipulated variable XI with low priority, which has not beenchecked, is selected in step I.

(4-2) It is determined in step II that the manipulated variable X1 isnot an offset manipulated variable→ to step III.

(4-3) In step III, the controlled variable Y3 which has not beenselected in association with the offset manipulated variable andcorresponds to only the manipulated variable X1 in the dependency tableis selected as a controlled variable to be combined (rule data (X1,Y3)). Check of all manipulated variables is not complete→ to step I.

(4—4) In step I, the manipulated variable X3 with low priority, whichhas not been checked yet, is selected.

(4-5) It is determined in step II that the manipulated variable X3 isnot an offset manipulated variable→ to 3.

(4-6) In step III, a controlled variable which has not been selected inassociation with the offset manipulated variable and corresponds to onlythe manipulated variable X3 in the dependency table is not present (norule data associated with X3). Check of all manipulated variables is notcomplete→ to step I.

(4-7) In step I, the manipulated variable X2 which has not been checkedyet is selected.

(4-8) It is determined in step II that the manipulated variable X2 is anoffset manipulated variable (check level= 0, rule flag= 0, check flag)→to step II-i.

(4-9) In step II-i, a controlled variable Y with high priority, whichhas not been selected yet, is selected→ to step II-ii.

(4-10) In step II-ii, the number of controlled variables which havealready been selected as rule data in correspondence with the selectedmanipulated variable X2 is “0” → to step II-ii-1.

(4-11) In step II-ii-1, no manipulated variables other than the selectedmanipulated variable X2 correspond to the selected controlled variableY1 (check 0 flag= 0)→ to step II-ii-2.

(4-12) In step II-ii-2, the manipulated variable X2 corresponds to thecontrolled variable Y1 (check 1 flag= 1)→ to step II-iii.

(4-13) In step II-iii, the number of controlled variables which havealready been selected as rule data in correspondence with the selectedmanipulated variable X2 is “0”. The current check level is “0”→ to stepII-iv-1.

(4-14) In step II-iv-1, rule flag= check 1 flag (rule data updating (X1,Y3 ), (X2, Y1))→ to step II-v.

(4-15) In step II-v, the rule flag is different from the initial value→step II-v-1.

(4-16) In step II-v-1, since the number of controlled variables whichhave been selected as rule data in correspondence with the selectedmanipulated variable X2 is “1”, check level is “2”. Updating processingin step II-v-3→ to step II-i.

(4-17) In step II-i, the controlled variable Y2 with high priority,which has not been selected yet, is selected→ to step II-ii.

(4-18) In step II-ii, the number of controlled variables which havealready been selected as rule data in correspondence with the selectedmanipulated variable X2 is “1” → to step II-ii-1.

(4-19) In step II-ii-1, no offset manipulated variables other than theselected manipulated variable X2 correspond to the selected controlledvariable Y2 (check 0 flag= 0)→ to step II-ii-2.

(4-20) In step II-ii-2, the manipulated variable X2 corresponds to thecontrolled variable Y2 (check 1 flag= 1)→ to step II-iii.

(4-21) In step II-iii, the number of controlled variables which havealready been selected as rule data in correspondence with the selectedmanipulated variable X2 is “1” → to step II-iii-1.

(4-22) In step II-iii-1, a gradient manipulated variable correspondingto the selected controlled variable Y2, which has not been employed asrule data, is present (X3, check 2 flag= 3)→ to step II-iii-2.

(4-23) In step II-iii-2, there are no gradient manipulated variablescorresponding to the selected controlled variable Y2 and correspondingto the selected controlled variable for another offset manipulatedvariable, which have not been employed as rule data. The current checklevel is “2” → to step II-iv-3.

(4-24) In step II-iv-3, rule flag= check 2 flag → to step II-v.

(for the manipulated variable X2, rule data updating (X1, Y3 ), (X2, Y1,Y2, X3, Y2)).

(4-25) In step II-v, the rule flag is different from the initial value,and two controlled variables have been selected. Updating processing instep II-v-3. Check level is “2” → to step II-vi-2.

(4-26) In step II-vi-2, both the controlled variables Y1 and Y2correspond to the manipulated variables X2 and X3. Correspondencebetween the manipulated variable and controlled variables to be adjustedby the manipulated variables cannot be determined. A flag (2)representing this is added (rule data updating (X1, Y3 ), (X2, Y1, Y2,X3, Y1, Y2, 2)).

(4-27) Check of all manipulated variables is complete.

5. Output of rule data

(X1, Y3 ), (X2, Y1, Y2, X3, Y1, Y2, 2)  . (4)

The adjustment rule format generating unit 12. arranges rule data(represented as in (4)) output from the adjustable controlled variableselection unit 11 in units of manipulated variables and changes the ruledata into a format which can be readily used for adjustment. Anadjustment rule format generation procedure will be described withreference to [Checklist 3] and FIG. 9. The format of the adjustment ruleis shown in [Format 1].

[Checklist 3] (FIG. 9)

I. Convert a manipulated variable with high priority into a rule format.

II. Has the manipulated variable been selected as rule data?

III. Pair adjustment rule (when the rule data has flag “2”). Theadjustment rule of a gradient manipulated variable is described next tothe adjustment rule of an offset manipulated variable.

IV. A manipulated variable which is not described in the rule data (allcorresponding controlled variables which have not been selected yet areselected).

[Format 1]

I. Manipulated variable number

II. Offset manipulated variable or gradient manipulated variable

III. Pattern of controlled variables

(III-i) Flag “2” is present at the end of the rule data

“2” for offset manipulated variable, and.

“1” for gradient manipulated variable

(III-ii) No flag “2” is present at the end of the rule data

“0” or both offset manipulated variable and

gradient manipulated variable

IV. Controlled variable number of adjustment object

V. Adjustment rule end identifier (−1)

As in the adjustable controlled variable selection unit 11, the flow ofprocessing in the adjustment rule format generating unit 12 for Table 4will be described below.

1. The manipulated variable X2 with high priority is selected from Table7.

2. On the basis of the rule associated with the manipulated variable X2in rule data (4), the adjustment rule (“20212-1”) associated with themanipulated variable X2 is described in accordance with the “adjustmentrule format”.

3. For the gradient manipulated variable X3 described in the ruleassociated with the manipulated variable X2 in rule data (4) as well,the adjustment rule (“31112-1”) associated with the manipulated variableX3 is additionally described in accordance with the “adjustment ruleformat”.

4. The manipulated variable X1 with high priority, which has not beenchecked yet, is selected from Table 7.

5. On the basis of the rule associated with the manipulated variable X1in rule data (4), the adjustment rule (“1103-1”) associated with themanipulated variable X1 is additionally described in accordance with the“adjustment rule format”.

6. Since all manipulated variables in Table 7 have been checked,processing is ended.

In this manner, the adjustment rule format generating unit 12 outputsthe adjustment rules in the format as shown in Table 9.

TABLE 9 EXAMPLE OF ADJUSTMENT RULES 2 0 2 1 2 −1 3 1 1 1 2 −1 1 1 0 3−1  

<Interpretation of Adjustment Rule>

Interpretation of the adjustment rule will be described next. Table 10describes the format of adjustment rule.

TABLE 10 FORMAT OF ADJUSTMENT RULE NUMBER OF OFFSET PATTERN NUMBER . . .ROW MANIPU- (0) OR OF CON- OF CON- END LATED GRADIENT TROLLED TROLLEDIDEN- VARIABLE (1) VARIABLE VARIABLE TI- TO BE TO BE TO BE FIERCORRECTED ADJUSTED ADJUSTED

Each row means an instruction (to be referred to as a rule hereinafter)for one manipulated variable. The respective columns in each row havethe following meanings. As shown in Table 10, the first elementrepresents “number of manipulated variable to be corrected”; the secondelement, “offset or gradient”; the third element, “pattern of controlledvariable to be adjusted”; and the fourth element, “numbers of controlledvariables to be adjusted”. “−1” at the end of each row means the end ofthe row (identifier indicating the end of the row). According to thisrule, “correct” and “adjust” mean that the manipulated variable iscorrected to adjust the controlled variable (to a desired value).

The first row of Table 9 is shown as Table 11, and this can beinterpreted as follows.

TABLE 11 FORMAT OF ADJUSTMENT RULE 2 0 2 1 2 −1

The number of the manipulated variable to be corrected is “2”, thetendency of change is “offset”, the pattern of the controlled variableto be adjusted is “2”, and the numbers of controlled variables to beadjusted is “1” and “2”.

In Table 9 as a whole, it is sequentially determined from the first rowwhether the adjustment rule indicated on each row is suitable for thesituation (whether the deviation of the controlled variable to bereferred to falls within the allowable range), and then, adjustment isperformed. The first row of Table 9 can be interpreted as described inTable 12.

TABLE 12 FORMAT OF ADJUSTMENT RULE FIRST ITEM “2” → “IN THIS RULE,SECOND MANIPU- LATED VARIABLE IS USED FOR ADJUSTMENT” SECOND ITEM “0” →“MANIPULATED VARIABLE OF THIS RULE IS OFFSET MANIPULATED VARIABLE” THIRDITEM “2” → “THIS RULE IS USED WHEN TWO OR MORE CONTROLLED VARIABLES OFCONTROLLED VARIABLES INDICATED BY ITEMS FROM FOURTH ITEM AREA OUT OFALLOWANCES” (CORRECTION AMOUNT IS SET ON THE BASIS OF SMALLEST ERROR)FOURTH ITEM “1” → “FIRST CONTROLLED VARIABLE (IF IT IS OUT OF ALLOWANCE)IS ADJUSTED AS FIRST PRIORITY” FIFTH ITEM “2” → “SECOND CONTROLLEDVARIABLE (IF IT IS OUT OF ALLOWANCE) IS ADJUSTED AS SECOND PRIORITY”SIXTH ITEM “−1” → “CONTROLLED VARIABLE AS ADJUSTMENT OBJECT OF THIS RULEIS NOT PRESENT ANYMORE”

As shown in Table 12, the first item, i.e., the first element is “2”,and this means that “in this rule, the second manipulated variable isused for adjustment”. The second item, i.e., the second element is “0”,and this means that “the manipulated variable of this rule is an offsetmanipulated variable”. The third item is “2”, and this means that “thisrule is used when two or more controlled variables indicated from thefourth item are out of the allowable ranges”. The correction amount isset on the basis of the smallest error. The fourth item is “1”, and thismeans that the first controlled variable (if it is out of the allowablerange) is adjusted as the first priority. The fifth item is “2”, andthis means that “the second controlled variable (if it is out of theallowable range) is adjusted as the second priority”. The sixth item is“−1”, and this means that “controlled variables as adjustment objects ofthis rule are not present anymore”.

Interpretation of the adjustment rule is based on three rules “rule 1)”to “rule 3)” shown in Table 13.

TABLE 13 INTERPRETATION OF ADJUSTMENT RULE RULE 1) WHEN BOTH FIRST ANDSECOND CONTROLLED VARIABLES ARE OUT OF ALLOWANCES, ADJUSTMENT ISPERFORMED USING SECOND MANIPULATED VARIABLE ON THE BASIS OF SMALLERERROR RULE 2) WHEN ONE OF FIRST AND SECOND CONTROLLED VARIABLES IS OUTOF ALLOWANCE, ADJUSTMENT IS PERFORMED USING THIRD MANIPULATED VARIABLERULE 3) WHEN THIRD CONTROLLED VARIABLE IS OUT OF ALLOWANCE, ADJUSTMENTIS PERFORMED USING FIRST MANIPULATED VARIABLE

“Rule 1)” defines that “when both the first and second controlledvariables are out of the allowable ranges, adjustment is performed usingthe second manipulated variable on the basis of the smaller error”.“Rule 2)” defines that “when one of the first and second controlledvariables is out of the allowable range, adjustment is performed usingthe third manipulated variable”. “Rule 3)” defines that “when the thirdcontrolled variable is out of the allowable range, adjustment isperformed using the first manipulated variable”.

<First Arrangement of Adjustment Control Apparatus 20>

FIG. 10 is a block diagram showing the basic arrangement of anadjustment control apparatus 20 according to the third or fourthembodiment. In order to obtain a desired output from the adjustmentobject 30, the adjustment control apparatus 20 inputs a correctionamount to an adjustment object 30 to change a variable parameter in theadjustment object 30 on the basis of the deviation between the outputfrom the adjustment object 30 and the desired value in accordance withan adjustment rule obtained from the adjustment rule generatingapparatus 10 according to the first or second embodiment.

<Second Arrangement of Adjustment Control Apparatus 20>

FIG. 11 shows the arrangement of the adjustment control apparatus 20according to the third embodiment. The adjustment control apparatus 20comprises an adjustment rule storage unit 21, a deviation datagenerating unit 22, an application rule determination unit 23, and amanipulated variable determination unit 24 (refer chart 1 shown in FIG.12).

The adjustment rule storage unit 21 stores adjustment rules obtainedfrom the adjustment rule storage unit 21. The deviation data generatingunit 22 calculates deviation (deviation amount) data from the desiredoutput value. The application rule determination unit 23 determines arule to be applied from the adjustment rules on the basis of thedeviation data. sent from the deviation data generating unit 22 on thebasis of the output from the adjustment object 30.

The manipulated variable determination unit 24 determines the value ofthe manipulated variable described in the rule determined by theapplication rule determination unit 23 and to be applied by multiplyingit by a predetermined proportional coefficient in accordance with thedeviation data obtained from the deviation data generating unit 22.

The case of the adjustment rules shown in Table 9 will be described.Assume that the adjustment object 30 has allowable deviations andsituations as shown in Table 14. Let Yi be the output from theadjustment object 30, and di the desired output value. The deviationobtained from the deviation data generating unit 22 is given by ei = di−Yi (i is a positive integer). This deviation data is input to theoutput data application rule determination unit 23 (allowable deviationof each controlled variable), so the application rule is determined inthe following manner.

TABLE 14 ALLOWABLE DEVIATION OF EACH CONTROLLED VARIABLE AND DEVIATIONAT CERTAIN TIME POINT CONTROLLED VARIABLE Y1 Y2 Y3 ALLOWABLE DEVIATION0.5 0.5 0.5 DEVIATION DATA 3.0 0.1 2.5

As shown in Table 14, the allowable deviations of the controlledvariables Yi are 0.5, 0.5, and 0.5. The deviations of the controlledvariables Yi at a certain time point are 3.0, 0.1, and 2.5. A controlledvariable Y2 falls within the allowable deviation, and the deviations ofcontrolled variables Y1 and Y3 are out of the allowable deviation.Referring to the adjustment rule shown in Table 9, for the first rule(“rule 1)”), the third item is “2”, and the controlled variables of theobject are “1”and “2”. The first rule is not applied to the case ofTable 14 because it is a rule applied when both the controlled variablesY1 and Y2 are out of the allowable deviations.

For the second rule (“rule 2)”), the third item is “1”, and thecontrolled variables of the object are “1” and “2”. The second rule isapplied when one of the deviations of the controlled variables Y1 and Y2is out of the allowable deviation, and this applies to the above case.At this time, the second rule indicates “1” in the first item, i.e.,that the controlled variable Y1 must be adjusted using the thirdmanipulated variable X3.

As described above, the second rule is applied as the adjustment rule,so rule “3 1 1 1 2−1” based on this application rule is transferred tothe manipulated variable determination unit 24.

The manipulated variable determination unit 24 calculates a correctionamount u3 of the manipulated variable X3 by multiplying a deviation e1of the controlled variable Y1 by a predetermined proportionalcoefficient k3 associated with the manipulated variable. If, in theadjustment rule, the manipulated variable is a gradient manipulatedvariable paired with an offset manipulated variable (when the third itemof the adjustment rule is “1”), the direct deviation e1 of thecontrolled variable Y1 is not multiplied by the proportionalcoefficient. Instead, in consideration of the controlled variable Y2which changes relative to the controlled variable Y1, the deviation ofthe controlled variable Y1 relative to the controlled variable Y2(difference between the deviation of the controlled variable Y1 and thatof the controlled variable Y2) is multiplied by the proportionalcoefficient k3, thereby calculating the correction amount of themanipulated variable (for a gradient manipulated variable which is notpaired with an offset manipulated variable, the correction amount iscalculated by simply multiplying the deviation by the proportionalcoefficient). More specifically,

u3=k3×(e1−e2) =−1.0×2.9=−2.9  (5)

(in this case, assume that k3 = −1.0)

With this processing, the adjustment object 30 has a new deviation.

The manipulated variable is repeatedly corrected in the above manner.When the deviations of all controlled variables converge within theallowable ranges, the application rule determination unit 23 determinesthat no rule need be applied, so the adjustment control apparatus 20does not correct the manipulated variable of the adjustment object 30.

As described above, in this embodiment, the adjustment control apparatus20 selects the manipulated variable to be corrected in accordance withthe adjustment rules shown in Table 9 on the basis of the deviation dataof the adjustment object 30, calculates the correction amount bymultiplying the deviation by the proportional coefficient, and inputsthe correction amount to the adjustment object 30. By repeating thisoperation, the deviations can be converged within the allowable range,thereby obtaining the desired output from the adjustment object 30.

(Fourth Embodiment)

<Third Arrangement of Adjustment Control Apparatus 20>

FIG. 13 shows the arrangement of an adjustment control apparatus 20according to the fourth embodiment. The adjustment control apparatus 20of this embodiment is characterized in that an adjustment history datastorage unit 25 is added to the adjustment control apparatus 20 of thethird embodiment.

The adjustment history data storage unit 25 stores input and outputvalues of an adjustment object 30 and deviation data obtained from adeviation data generating unit 22.

A manipulated variable determination unit 24 determines the input to theadjustment object 30 with reference to not only the input and outputvalues of the adjustment object 30 but also data in the adjustmenthistory data storage unit 25 (refer chart 1 shown in FIG. 12).

The adjustment history data storage unit 25 stores adjustment historydata as shown in Table 15 (deviation at adjustment count “0” is aninitial deviation).

TABLE 15 ADJUSTMENT HISTORY ADJUST- CORRECTION DEVIATION AFTER MENTAMOUNT ADJUSTMENT COUNT u1 u2 u3 e1 e2 e3 0 0.0 0.0 0.0 3.0 0.1 2.5 10.0 0.0 −2.9 1.5 1.0 2.0 2 0.0 −1.0 0.0 −0.9 −1.4 −0.4 3 0.0 0.9 0.0 1.20.8 1.8 4 0.0 −0.8 0.0 −0.7 −1.1 −0.1 . . . . . . . . .

In this data, in the first adjustment, rule 2 in adjustment rule table 9is selected in correspondence with the initial deviation, and amanipulated variable X3 is corrected by −2.9. From the secondadjustment, rule 1 is selected, and a manipulated variable X2 isrepeatedly corrected (since the manipulated variable X2 is an offsetmanipulated variable, the correction amount is calculated by directlymultiplying the smaller one of the allowable deviations of controlledvariables Y1 and Y2, which are out of the allowable ranges, by aproportional coefficient) (in this case, a proportional coefficient k2for defining the correction amount of the manipulated variable X2 is setto be −1.0).

In this embodiment, the manipulated variable determination unit 24refers to the adjustment history data. If the deviation vibrates betweenpositive and negative values upon correcting the same manipulatedvariable, the proportional coefficient of the manipulated variable ismultiplied by a weight to determine the manipulated variable. As in theabove example, when the same manipulated variable is repeatedlycorrected three or more times, and the deviation vibrates betweenpositive and negative values, for the second manipulated variable, theproportional coefficient is multiplied by a weight of 0.5. In this case,the proportional coefficient is corrected as follows: $\begin{matrix}\begin{matrix}{k_{i}^{\prime} = {0.5 \times k_{i}}} \\{= {0.5 \times \left( {- 1.0} \right)}}\end{matrix} & (6) \\{\quad {= {- 0.5}}\quad} & (7) \\{k_{i}^{def} = k_{i}^{\prime}} & (8)\end{matrix}$

With this processing, adjustment contents as shown in Table 16 areexpected to be obtained from the fifth adjustment (that is, alldeviations fall within the allowable ranges in the sixth adjustment).

TABLE 16 ADJUSTMENT HISTORY ADJUST- CORRECTION DEVIATION AFTER MENTAMOUNT ADJUSTMENT COUNT u1 u2 u3 e1 e2 e3 . . . . . . . . . 4 0.0 −0.80.0 −0.7 −1.1 −0.1 5 0.0 0.35 0.0 0.1 −0.3 0.7 6 −0.7 0.0 0.0 0.4 0.40.1

A proportional coefficient k1 for defining the correction amount of amanipulated variable X1 is set to be −1.0.

As described above, the manipulated variable determination unit 24 ofthis embodiment has a function of not only determining the correctionamount proportional to the manipulated variable but also exceptionallydetermining the correction amount of the manipulated variable inaccordance with conditions in correspondence with the outputcharacteristics of the adjustment object 30 in the adjustment process.

An example in which the deviation of the adjustment object 30 vibratesbetween positive and negative values upon repeatedly correcting the samemanipulated variable has been described above. In addition, processingas shown in Table 17 is performed in accordance with conditions.

TABLE 17 EXCEPTIONAL PROCESSING CONDITION PROCESSING DEVIATION VIBRATESPROPORTIONAL BETWEEN POSITIVE AND COEFFICIENT IS NEGATIVE VALUES UPONMULTIPLIED BY WEIGHT REPEATEDLY CORRECTING (0 TO 1) (ABOVE THE SAMEMANIPULATED EXAMPLE) VARIABLE DEVIATION HAVING THE PROPORTIONAL SAMESIGN IS COEFFICIENT IS REPEATEDLY OBTAINED MULTIPLIED BY WEIGHT UPONREPEATEDLY (1 OR MORE) CORRECTING THE SAME MANIPULATED VARIABLEDEVIATION INCREASES SIGN OF PROPORTIONAL WITHOUT CHANGING SIGNCOEFFICIENT IS UPON CORRECTING INVERTED MANIPULATED VARIABLE DEVIATIONDOES NOT ANOTHER CHANGE UPON PREDETERMINED (ONE CORRECTING OR MORE)MANIPULATED MANIPULATED VARIABLE VARIABLE IS CORRECTED BY PREDETERMINEDAMOUNT

Table 17 shows exceptional processing condition processing. When “thedeviation vibrates between positive and negative values upon repeatedlycorrecting the same manipulated variable”, “the proportional coefficientis multiplied by a weight (0 to 1)”. When “a deviation with the samesign is repeatedly obtained upon repeatedly correcting the samemanipulated variable”, “the proportional coefficient is multiplied by aweight (1 or more)”. When “the deviation increases without changing thesign upon correcting the manipulated variable”, “the sign of theproportional coefficient is inverted”. When “the deviation does notchange upon correcting the manipulated variable”, “other (one or more)predetermined manipulated variables are corrected by a predeterminedamount”.

<Fourth Arrangement of Adjustment Control Apparatus 20>

FIG. 14 is a block diagram showing the arrangement of an adjustmentcontrol apparatus 20 according to the fifth embodiment. This embodimentis characterized in that history data is positively used in themanipulated variable determination unit 24 of the fourth embodiment. Theapparatus has almost the same arrangement as that of the fourthembodiment (the difference from the arrangement shown in FIG. 12 is thatan application rule determination unit 23 refers to history data) exceptthe data and manipulated variable determination scheme.

In the manipulated variable determination unit 24 of this embodiment,the correction amount of the manipulated variable is not simplydetermined in proportion to the deviation, unlike the fourth embodiment,but calculated using past history data (chart 2 shown in FIG. 15).

More specifically, assume that, for the initial deviation shown in Table14, a manipulated variable X3 is to be corrected on the basis of “rule2”. One adjustment is sacrificed to perform “test adjustment” for atrial to correct the manipulated variable by a predetermined amount.Simultaneously, the deviation of the adjustment object after correctionof the manipulated variable is calculated, and history data as shown inTable 18 is stored in a history data storage unit 25. In this case, atest correction amount u3test is set to be 1.0.

TABLE 18 ADJUSTMENT HISTORY ACTUAL ADJUST- MENT (0) DEVIATION ADJUST- ORTEST CORRECTION AFTER MENT ADJUST- AMOUNT ADJUSTMENT COUNT MENT (1) u1u2 u3 e1 e2 e3 0 0 0.0 0.0 0.0 3.0 0.1 2.5 1 1 0.0 0.0 1.0 3.5 −0.3 2.7

In adjustment history shown in Table 18, the manipulated variable is notcorrected at adjustment count “0”, although it means “0” of actualadjustment for the convenience of data format.

The adjustment control apparatus 20 of this embodiment is characterizedin that data at adjustment count “1” is used in the situation shown inTable 18 to obtain the correction amount of the manipulated variable X3at adjustment count “2”, and adjustment is performed using thecorrection amount as a determination reference.

Details will be described.

First, the application rule determination unit 23 refers to the historydata stored in the history data storage unit 25. If the currentsituation is immediately after test adjustment, the next processing isactual adjustment. Therefore, the rule selected for the previous testadjustment is determined as an application rule independently of thedeviation after test adjustment.

The manipulated variable determination unit 24 determines the correctionamount of the manipulated variable using data before and after testadjustment in the following manner. According to “rule 2”, themanipulated variable X3 corresponds to controlled variables Y1 and Y2.The controlled variable Y1 is to be corrected by the manipulatedvariable X3. As described above in the third embodiment, the manipulatedvariable X3 is a gradient manipulated variable paired with an offsetmanipulated variable, as is apparent from “rule 2”, so the manipulatedvariable X3 changes the corresponding controlled variables Y1 and Y2relative to each other.

The relative change amount between the deviation of the controlledvariable Y1 and that of the controlled variable Y2 before and after testadjustment is calculated. The correction amount of the manipulatedvariable X3 is calculated on the basis of equation (9) such that therelative deviation component (difference between the deviation of thecontrolled variable Y1 and that of the controlled variable Y2) betweenthe current controlled variable Y1 and the controlled variable Y2 iscanceled. In Table 18, $\begin{matrix}\begin{matrix}{u_{3} = {{{- \gamma} \times \frac{e_{1} - e_{2}}{{\Delta \quad e_{1}} - {\Delta \quad e_{2}}} \times u_{3}^{test}} - u_{3}^{test}}} \\{= {{{- 1.0} \times \frac{3.0 - 0.1}{\left( {3.5 - 3.0} \right) - \left( {{- 0.3} - 0.1} \right)} \times 1.0} - 1.0}}\end{matrix} & (9) \\{{\cong {- 4.2}}\quad} & (10)\end{matrix}$

where γ is a coefficient as the reliability of the calculated amount.This coefficient will be called a reliability coefficient in distinctionfrom the proportional coefficient in the third or fourth embodiment.

Assume that when this correction amount is applied to an adjustmentobject 30, the history data is updated, and an adjustment history asshown in Table 19 is obtained.

TABLE 19 ADJUSTMENT HISTORY ACTUAL ADJUST- MENT (0) DEVIATION ADJUST- ORTEST CORRECTION AFTER MENT ADJUST- AMOUNT ADJUSTMENT COUNT MENT (1) u1u2 u3 e1 e2 e3 0 0 0.0 0.0 0.0 3.0 0.1 2.5 1 1 0.0 0.0 1.0 3.5 −0.3 2.72 0 0.0 0.0 −4.2 1.4 0.9 2.0

As in the case of the third embodiment, the deviation of the controlledvariable Y2 falls out of the allowable deviation, so the applicationrule determination unit 23 selects “rule 1” for the next adjustment(i.e., test adjustment). According to “rule 1”, the manipulated variableX2 is selected. In test adjustment, a predetermined test correctionamount, and in this case, u3test = 1.0, is applied to the adjustmentobject 30, and the deviation of the controlled variable is updated shownin Table 20.

TABLE 20 ADJUSTMENT HISTORY ACTUAL ADJUST- MENT (0) DEVIATION ADJUST- ORTEST CORRECTION AFTER MENT ADJUST- AMOUNT ADJUSTMENT COUNT MENT (1) u1u2 u3 e1 e2 e3 0 0 0.0 0.0 0.0 3.0 0.1 2.5 1 1 0.0 0.0 1.0 3.5 −0.3 2.72 0 0.0 0.0 −4.2 1.4 0.9 2.0 3 1 0.0 1.0 0.0 3.6 3.1 4.2

In actual adjustment, deviation data before and after test adjustment isused. Since the manipulated variable X2 is an offset manipulatedvariable, the change amount in the smaller one of the deviations ofcontrolled variables to be adjusted is directly used. More specifically,$\begin{matrix}\begin{matrix}{u_{2}\quad = \quad {{{- \beta} \times \quad \frac{\min \quad \left\{ {e_{1},\quad e_{2}} \right\}}{\Delta \quad \min \quad \left\{ {e_{1},\quad e_{2}} \right\}} \times u_{2}^{test}}\quad - \quad u_{2}^{test}}} \\{= {{{- \beta} \times \frac{e_{2}}{\Delta \quad e_{2}} \times u_{2}^{test}}\quad - \quad u_{2}^{test}}} \\{= {{{- 1.0} \times \frac{0.9}{3.1\quad - \quad 0.9} \times 1.0}\quad - \quad 1.0}}\end{matrix} & (11) \\{{{\cong \quad} - 1.4}\quad} & (12)\end{matrix}$

where β is the reliability coefficient for the manipulated variable X2and is set to be 1.0.

When this correction amount is applied to the adjustment object 30, thehistory data is updated, and adjustment history as shown in Table 21 isobtained.

TABLE 21 ADJUSTMENT HISTORY ACTUAL ADJUST- MENT (0) OR TEST DEVIATIONADJUST- ADJUST- CORRECTION AFTER MENT MENT AMOUNT ADJUSTMENT COUNT (1)u1 u2 u3 e1 e2 e3 0 0 0.0 0.0 0.0 3.0 0.1 2.5 1 1 0.0 0.0 1.0 3.5 −0.32.7 2 0 0.0 0.0 −4.2 1.4 0.9 2.0 3 1 0.0 1.0 0.0 3.6 3.1 4.2 4 0 0.0−1.4 0.0 0.4 −0.1 1.0

In Table 21, only a controlled variable Y3 is out of the allowabledeviation. For the next test adjustment, adjustment rule 3 is selected,and a manipulated variable X1 is selected as a manipulated variable tobe corrected. Assume that a test correction amount u1test is 1.0, andthe history data as shown in Table 22 is obtained after test adjustmentof the adjustment object 30

TABLE 22 ADJUSTMENT HISTORY ACTUAL ADJUST- MENT (0) OR TEST DEVIATIONADJUST- ADJUST- CORRECTION AFTER MENT MENT AMOUNT ADJUSTMENT COUNT (1)u1 u2 u3 e1 e2 e3 0 0 0.0 0.0 0.0 3.0 0.1 2.5 1 1 0.0 0.0 1.0 3.5 −0.32.7 2 0 0.0 0.0 −4.2 1.4 0.9 2.0 3 1 0.0 1.0 0.0 3.6 3.1 4.2 4 0 0.0−1.4 0.0 0.4 −0.1 1.0 5 1 1.0 0.0 0.0 0.4 0.1 1.9

According to “rule 3”, the manipulated variable X1 is a gradientmanipulated variable which is not paired with an offset manipulatedvariable. Therefore, like the offset manipulated variable, the changeamount of the smallest one of the deviations of corresponding controlledvariables before and after test adjustment is used to calculate thecorrection amount of the manipulated variable. $\begin{matrix}\begin{matrix}{u_{1}\quad = \quad {{{- \alpha} \times \quad \frac{\min \quad \left\{ e_{3} \right\}}{\Delta \quad \min \quad \left\{ e_{3} \right\}} \times u_{1}^{test}}\quad - \quad u_{1}^{test}}} \\{= {{{- 1.0} \times \frac{1.0}{1.9\quad - \quad 1.0} \times 1.0}\quad - \quad 1.0}}\end{matrix} & (13) \\{{{\cong \quad} - 2.1}\quad} & (14)\end{matrix}$

This correction amount is applied to the adjustment object 30, thehistory data is updated, and adjustment history as shown in Table 23 isobtained.

TABLE 23 ADJUSTMENT HISTORY ACTUAL ADJUST- MENT (0) DEVIATION ADJUST- ORTEST CORRECTION AFTER MENT ADJUST- AMOUNT ADJUSTMENT COUNT MENT (1) u1u2 u3 e1 e2 e3 0 0 0.0 0.0 0.0 3.0 0.1 2.5 1 1 0.0 0.0 1.0 3.5 −0.3 2.72 0 0.0 0.0 −4.2 1.4 0.9 2.0 3 1 0.0 1.0 0.0 3.6 3.1 4.2 4 0 0.0 −1.40.0 0.4 −0.1 1.0 5 1 1.0 0.0 0.0 0.4 −0.1 1.9 6 0 −2.1 0.0 0.0 0.4 −0.1−0.1

In Table 23, all deviations fall within the allowable deviations. Forthis reason, in the next test adjustment, the application ruledetermination unit 23 determines “no application rule”, and correctionof the manipulated variable of the adjustment object 30 is ended.

In this embodiment, exceptional processing by the manipulated variabledetermination unit 24 can be performed, as in the fifth embodiment.Especially, in this embodiment, adjustment is separately performed intest adjustment and actual adjustment, so an example in whichexceptional processing in the fifth embodiment is separated into testadjustment and actual adjustment, and the proportional coefficient isreplaced with the reliability coefficient will be described.

This corresponds to processing contents as shown in Table 24.

TABLE 24 EXCEPTIONAL PROCESSING CONDITION PROCESSING DEVIATION VIBRATESPROPORTIONAL COEFFICIENT BETWEEN POSITIVE AND IS MULTIPLIED BY WEIGHTNEGATIVE VALUES UPON (0 TO 1) REPEATEDLY CORRECTING THE SAME MANIPULATEDVARIABLE IN ACTUAL ADJUSTMENT DEVIATION HAVING THE RELIABILITYCOEFFICIENT SAME SIGN IS IS MULTIPLIED BY WEIGHT REPEATEDLY OBTAINED (1OR MORE) UPON REPEATEDLY CORRECTING THE SAME MANIPULATED VARIABLE INACTUAL ADJUSTMENT DEVIATION INCREASES SIGN OF RELIABILITY WITHOUTCHANGING COEFFICIENT IS INVERTED SIGN UPON ACTUAL ADJUSTMENT DEVIATIONDOES NOT ANOTHER PREDETERMINED CHANGE UPON TEST (ONE OR MORE) ADJUSTMENTMANIPULATED VARIABLE IS CORRECTED BY PREDETERMINED AMOUNT IN ACTUALADJUSTMENT (DEAD BAND) DEVIATION DOES NOT ANOTHER PREDETERMINED CHANGEUPON ACTUAL (ONE OR MORE) ADJUSTMENT MANIPULATED VARIABLE IS CORRECTEDBY PREDETERMINED AMOUNT IN ACTUAL ADJUSTMENT (DEAD BAND)

When “the deviation vibrates between positive and negative values uponrepeatedly correcting the same manipulated variable in actualadjustment”, “the reliability coefficient is multiplied by a weight (0to 1)”. When “a deviation with the same sign is repeatedly obtained uponrepeatedly correcting the same manipulated variable in actualadjustment”, “the reliability coefficient is multiplied by a weight (1or more)”. When “the deviation increases without changing the sign uponactual adjustment”, “the sign of the reliability coefficient isinverted”. When “the deviation does not change in test adjustment”,“other (one or more) predetermined manipulated variables are correctedby a predetermined amount in actual adjustment (dead band)”. When “thedeviation does not change in actual adjustment”, “other (one or more)predetermined manipulated variables are corrected by a predeterminedamount in actual adjustment (dead band)”.

The manipulated variable determination unit 24 may continuously performtest adjustment or actual adjustment, as needed. This operation iseffective when the manipulated variable is limited within an infiniterange, or adjustment is performed on an assumption of repetitiveoperation under a situation where the operation is likely to fail.

(Sixth Embodiment)

<Arrangement of Adjustment Possibility Evaluation Apparatus 40>

FIG. 16 shows the arrangement of an adjustment possibility evaluationapparatus 40 of the sixth embodiment. The adjustment possibilityevaluation apparatus 40 evaluates whether the adjustment object can beadjusted by an adjustment rule on the basis of the “adjustment rules”described in the second embodiment.

In the rule generating apparatus described in the second embodiment,controlled variables and manipulated variables for adjusting thecontrolled variables do not always correspond in one-to-onecorrespondence depending on the manner for preparing the dependencytable. In such a case, the rule generating apparatus of the secondembodiment lists up all controlled variables that the apparatus can copewith.

In such a case, the adjustment possibility evaluation apparatus 40 ofthe sixth embodiment changes adjustment rules such that all controlledvariables can be adjusted independently of the deviation situation.

A dependency table shown in Table 25 shows the characteristics of theadjustment object, which are different from those in Table 4 (the numberof inputs/outputs is 3).

TABLE 25 DEPENDENCY TABLE 3 1 1 1 0 1 1 1 1 0 1 1 0

Accordingly, As shown in Table 26, adjustment rules obtained by the rulegenerating apparatus of the second embodiment are also different fromthose in Table 9.

TABLE 26 ADJUSTMENT RULE CORRESPONDING TO DEPENDENCY TABLE IN TABLE 22 21 0 2 3 −1 1 1 0 2 3 −1 3 1 0 1 −1

The adjustment rule in this format indicates that “even when thedeviations of both the second and third controlled variables are out ofthe allowable ranges, and the second controlled variable is adjusted bythe second manipulated variable to converge the deviation of the secondcontrolled variable within the allowable range, adjustment is furtherperformed using the second manipulated variable unless the deviation ofthe third controlled variable simultaneously falls within the allowablerange” (this is because a condition for the deviations of a plurality ofcontrolled variables to be adjusted by the manipulated variable is notdesignated as the third item of each rule is “0”).

With this processing, the deviation of the third controlled variable,which has been adjusted once, may increase, and the adjustmentpossibility is not guaranteed. In this case, the adjustment possibilityevaluation apparatus 40 of this embodiment changes the format such thatthe controlled variables and manipulated variables correspond inone-to-one correspondence in adjustment, i.e., the second controlledvariable is adjusted by the second manipulated variable, and the thirdcontrolled variable is adjusted by the first manipulated variable.

The arrangement of the adjustment possibility evaluation apparatus 40and the flow of processing by this arrangement will be described belowwith reference to the block diagram shown in FIG. 16. As shown in FIG.16, the adjustment possibility evaluation apparatus 40 comprises a rulecandidate initial setting unit 41, a controlled variable selection unit42, a corresponding manipulated variable search unit 43, a rulecandidate generating unit 44, and a rule group generating unit 45.

The rule candidate initial setting unit 41 performs initial setting ofrule candidates. The rule candidate initial setting unit 41 determinesthe pattern of the selection order of controlled variables andinitializes the correspondence data between the controlled variables andmanipulated variables. In the dependency table shown in Table 25, theadjustment object has three controlled variables. First, the order forchecking whether the controlled variables can be adjusted is determined.In this case, the-number of orders is:

₃P₃=3!=6  (15)

At this time, the selection order of controlled variables is set as rulecandidate data as shown in Table 27. One sequence of controlledvariables corresponds to one rule candidate.

Currently, the correspondence between the controlled variable andmanipulated variable is not checked for any one of candidate numbers 1to 6, so “0” is set in the item “checked”.

TABLE 27 RULE CANDIDATE DATA CANDIDATE NUMBER ORDER OF SELECTION CHECKED1 1 2 3 0 2 1 3 2 0 3 2 1 3 0 4 2 3 1 0 5 3 1 2 0 6 3 2 1 0

Simultaneously, data representing correspondence between a controlledvariable and a manipulated variable for adjusting the controlledvariable is initialized (Table 28).

TABLE 28 CORRESPONDENCE DATA MANIPU- CONTROLLED LATED VARIABLE VARIABLEY1 Y2 Y3 X1 0 0 0 X2 0 0 0 X3 0 0 0

Next, the controlled variable selection unit 42 selects one controlledvariable which does not correspond to any manipulated variable(controlled variable for which all correspondence data for manipulatedvariables are “0”) in correspondence data from rule candidates with highpriority (with small candidate numbers) which have not been checked(“0”). In this case, a first controlled variable Y1 as candidate 1 isselected.

The corresponding manipulated variable search unit 43 selects amanipulated variable corresponding to the selected first controlledvariable on the basis of the input adjustment rule shown in Table 26 andupdates the correspondence data in Table 28. More specifically, Table 26is searched from the upper rule for a manipulated variable correspondingto the first controlled variable Y1. The correspondence data is updatedon the basis of the search result. In this case, only a thirdmanipulated variable X3 corresponds to the controlled variable Y1, sothe correspondence data is updated like in Table 29.

TABLE 29 CORRESPONDENCE DATA MANIPU- CONTROLLED LATED VARIABLE VARIABLEY1 Y2 Y3 X1 0 0 0 X2 0 0 0 X3 1 0 0

Since all controlled variables of the selected rule candidate have notbeen checked yet, the flow returns to processing in the controlledvariable selection unit 42 to select the next controlled variable.

The controlled variable at the second order is a second controlledvariable Y2. A corresponding manipulated variable is checked on thebasis of the adjustment rules, as described above. The secondmanipulated variable is selected according to rule 1.

The same processing is repeated for the third controlled variable of thethird order, and the adjustment rules are checked. A third controlledvariable Y3 also corresponds to the second controlled variable on thebasis of rule 1. However, since the second manipulated variable hasalready been designated to correspond to the second controlled variable,the third controlled variable cannot correspond to the secondmanipulated variable. In this case, rule 2 also corresponds to the thirdcontrolled variable, and eventually, the first manipulated variable ismade to correspond to the third controlled variable. With the abovecheck processing, the correspondence data is updated to Table 30.

TABLE 30 CORRESPONDENCE DATA IN RULE CANDIDATE 1 MANIPU- CONTROLLEDLATED VARIABLE VARIABLE Y1 Y2 Y3 X1 0 0 1 X2 0 1 0 X3 1 0 0

The rule candidate generating unit 44 searches for the correspondencedata from the manipulated variables in accordance with the order ofadjustment rules shown in Table 26, converts the table into a format asshown in Table 26, and stores it. In this case, Table 31 is obtained.That is, adjustment rules shown in Table 31 are obtained incorrespondence with “rule candidate 1” and stored.

TABLE 31 ADJUSTMENT RULE CORRESPONDING TO RULE CANDIDATE 1 2 1 0 2 −1 11 0 3 −1 3 1 0 1 −1

Check of the correspondence for “rule candidate 1” is complete, althoughcandidates 2 to 6 have not been checked yet. The rule candidate initialsetting unit 41 changes “0” of rule candidate 1 which has already beenchecked to “1” in the rule candidate data shown in Table 27. The rulecandidate generating unit 44 also initializes the correspondence tableto the state shown in Table 28 for “rule candidate 2”.

The above processing is repeated for “rule candidate 2”. Since the checkorder of controlled variables changes such that the second controlledvariable Y2 and the third controlled variable Y3 are exchanged,candidate rules shown in Table 32 are obtained.

TABLE 32 ADJUSTMENT RULE CORRESPONDING TO RULE CANDIDATE 2 2 1 0 2 −1 11 0 3 −1 3 1 0 1 −1

Similarly, “rule candidate 3” to “rule candidate 6” are also checked,and candidate rules as shown in Table 33 are stored in the rulecandidate generating unit 44.

TABLE 33 CANDIDATE RULE OF RULE CANDIDATE GENERATING UNIT 44 CANDIDATENUMBER CANDIDATE RULE 1 2 1 0 2 −1 1 1 1 0 3 −1 1 3 1 0 1 −1 2 2 1 0 3−1 2 1 1 0 2 −1 2 3 1 0 1 −1 3 2 1 0 2 −1 3 1 1 0 3 −1 3 3 1 0 1 −1 4 21 0 2 −1 4 1 1 0 3 −1 4 3 1 0 1 −1 5 2 1 0 3 −1 5 1 1 0 2 −1 5 3 1 0 1−1 6 2 1 0 3 −1 6 1 1 0 2 −1 6 3 1 0 1 −1

The rule group generating unit 45 omits same rules from the storedcandidate rules and outputs the remaining as a rule group (Table 34).

TABLE 34 RULE GROUP (OUTPUT) CANDIDATE NUMBER CANDIDATE RULE 1 2 1 0 2−1 1 1 1 0 3 −1 1 3 1 0 1 −1 2 2 1 0 3 −1 2 1 1 0 2 −1 2 3 1 0 1 −1

(Seventh Embodiment)

<Arrangement of Automatic Adjustment Rule Candidate Generating Apparatus50>

FIG. 17 shows the arrangement of an automatic adjustment rule candidategenerating apparatus 50 according to the seventh embodiment. As shown inFIG. 17, the automatic adjustment rule candidate generating apparatus 50comprises a dependency table candidate generating unit 51, a dependencytable/rule candidate storage unit 52, and an adjustment rule generatingapparatus 10.

When the input/output data of an adjustment object 30 can be used, theautomatic adjustment rule candidate generating apparatus 50automatically prepares one or more “dependency tables” directly from theinput/output data, thereby generating adjustment rules as candidatesusing the adjustment rule generating apparatus 10 described in thesecond embodiment.

Assume that actual adjustment object data in Table 35 is given asinput/output data.

TABLE 35 ACTUAL DATA OF ADJUSTMENT OBJECT MANIPULATED VARIABLECONTROLLED VARIABLE (INPUT DATE) DEVIATION (OUTPUT DATA) X1 X2 X3 e1 e2e3 0.0 0.0 0.0 −0.1 −0.2 0.0 1.0 0.0 0.0 0.1 −2.0 2.0 0.0 1.0 0.0 1.4−0.4 1.4 0.0 0.0 1.0 0.4 −1.0 −1.0

The dependency table candidate generating unit 51 calculates, from theactual data shown in Table 35, data of a change amount Aei of thedeviation of a controlled variable from the change amount of eachmanipulated variable ΔYi, as shown in Table 36 (i is a positiveinteger).

TABLE 36 INPUT/OUTPUT CHARACTERISTIC CHANGE ACTUAL DATA CHANGE AMOUNT OFCHANGE AMOUNT OF MANIPULATE CONTROLLED VARIABLE VARIABLE DEVIATION ΔX1ΔX2 ΔX3 Δe1 Δe2 Δe3 1.0 0.0 0.0 0.2 0.0 2.0 0.0 1.0 0.0 1.5 1.6 1.4 0.00.0 1.0 0.5 1.0 −1.0

Normally, the actual data of the adjustment object is not alwaysindependent data in units of manipulated variables first, as in Table35. In addition, the quantity of data is often large. At this time,conversion to Table 36 can be realized by performing linearapproximation using the method of least squares in association with therelationship between the change amount of the manipulated variable andthat of the deviation of the controlled variable.

A) Quantization Processing

For the purpose of conversion into the dependency table, data isquantized in units of ±U for the change amount of the deviation in Table36. When U=0.3, data shown in Table 37 is obtained.

TABLE 37 QUANTIZED INPUT/OUTPUT CHARACTERISTIC CHANGE ACTUAL DATA (U =0.3) CHANGE AMOUNT OF CHANGE AMOUNT OF MANIPULATED (DEVIATION VARIABLEQUANTIZATION UNIT) ΔX1 ΔX2 ΔX3 Δe1 Δe2 Δe3 1.0 0.0 0.0 0 0 6 0.0 1.0 0.05 5 4 0.0 0.0 1.0 1 3 −3

B) Dependency Estimation

In Table 37,

1. a manipulated variable for which controlled variables changing in asimilar manner (controlled variables having a value within the range of±N for quantization unit; in this case, N = 1) are present is regardedas an offset manipulated variable (“0”); otherwise, a gradientmanipulated variable (“1”).

2. “1” is set in items other than “0” in the dependency table.

In addition, data of the number of inputs/outputs, i.e., “3” is added.With this processing, Table 38 as a dependency table reflecting theactual adjustment object data in Table 35 can be obtained, like thedependency table shown in Table 4 described in the second embodiment.

TABLE 38 DEPENDENCY TABLE 1 3 1 0 1 0 1 1 0 1 1 1 1 1

Table 38 is obtained by setting U=0.3 for “A) quantization processing”and N=1 for “B) dependency estimation”. When these parameters change,the resultant dependency table changes, as a matter of course. Thedependency table candidate generating unit 51 prepares combinations ofparameters in advance, generates dependency tables in correspondencewith these combinations, and stores them in the dependency table/rulecandidate storage unit 52.

In this case, assume that the set of parameters are given by Table 39.

TABLE 39 PARAMETER FOR GENERATING DEPENDENCY TABLE CORRESPONDINGDEPENDENCY TABLE NUMBER U N 1 0.3 1 2 0.1 4

The dependency table of “number 1” has been generated above. Thedependency table candidate generating unit 51 further generates thedependency table of “number 2” in accordance with Table 39. When Table36 is quantized using U=0.1, Table 40 is obtained.

TABLE 40 QUANTIZED INPUT/OUTPUT CHARACTERISTIC CHANGE ACTUAL DATA CHANGEAMOUNT OF CHANGE AMOUNT OF MANIPULATED DEVIATION VARIABLE QUANTIZATIONUNIT) ΔX1 ΔX2 ΔX3 Δe1 Δe2 Δe3 1.0 0.0 0.0 2 0 20 0.0 1.0 0.0 15 16 140.0 0.0 1.0 5 10 −10

When dependency estimation is performed for Table 40 using N=4, adependency table as shown in Table 41 is obtained. The dependencytable/rule candidate storage unit 52 inputs the dependency tablesgenerated by the dependency table candidate generating unit 51 to theadjustment rule generating apparatus 10 described in the secondembodiment and causes the adjustment rule generating apparatus 10 tooutput adjustment rules corresponding to the dependency tables. Withthis operation, the adjustment rules are output as adjustment rulecandidates.

TABLE 41 DEPENDENCY TABLE 2 3 1 0 1 1 1 1 0 1 1 1 1 1

In this manner, adjustment rules as shown in Tables 42 and 43 can beobtained from the dependency table shown in Table 41.

TABLE 42 ADJUSTMENT RULES CORRESPONDING TO DEPENDENCY TABLE 1 2 0 2 1  2 −1 3 1 1 1   2 −1 1 1 0 3 −1

TABLE 43 ADJUSTMENT RULES CORRESPONDING TO DEPENDENCY TABLE 2 2 0 0 2 −13 1 0 3 −1 1 1 0 1 −1

FIGS. 19A and 19B show correction of adjustable parameter gains k1 to k3of a certain object when the characteristics of the object are adjustedto desired characteristics. FIG. 19A shows a state wherein the gains k1to k3 become constant near “3” of the abscissa (axis corresponding tothe correction count), and adjustment is complete. In FIG. 19A, if thegain k1 is gradually corrected over “2”, the characteristics of theobject are not improved, and adjustment of the gain k1 is repeated anumber of times. It can be estimated that the object has a dead band forthe gain k1.

FIG. 19B shows a state wherein the gains k1 to k3 finally becomeconstant, and adjustment is complete. FIG. 19B also shows a statewherein the gain k2 is repeatedly corrected with wasteful oscillations.

According to the present invention, the dead band in adjustment and thevibration phenomenon due to the nonlinearity of the object as shown inFIGS. 19A and 19B can be avoided, as shown in FIG. 19C. The desiredadjusted state can be quickly ensured upon applying the presentinvention.

The present invention can also be stored in a recording medium assoftware which can be read and executed by a computer, and distributed.

As has been described above, according to the present invention, in theadjustment operation which requires human determination assuming trialand error, the adjustment procedure is directly established from data ofqualitative input/output relationship of the adjustment object or actualadjustment object data. With this arrangement, trial and error inpreparation of the adjustment procedure or in adjustment is reduced,thereby standardizing or automating the adjustment operation.

Additional advantages and modifications will readily occurs to thoseskilled in the art. Therefore, the invention in its broader aspects isnot limited to the specific details and representative embodiments shownand described herein. Accordingly, various modifications may be madewithout departing from the spirit or scope of the general inventiveconcept as defined by the appended claims and their equivalents.

What is claimed is:
 1. A method for generating an adjustment rule usedfor an adjustment of making a first data group corresponding to outputsof a predetermined object have a desired value by determining a seconddata group corresponding to inputs to said object, the methodcomprising: obtaining, from a dependency table which represents adependency between said first data group and said second data group,predetermined first data influenced by predetermined second data, thepredetermined first data corresponding to the output of said object andthe predetermined second data corresponding to the inputs to saidobject, the dependency table being based on actual data obtained fromsaid object; selecting specific second data on the basis of thepredetermined first data influenced by the predetermined second data andsimultaneously selecting specific first data on the basis of thepredetermined second data influencing the predetermined first data; andcombining said specific second data and said specific first data togenerate an adjustment rule, wherein said adjustment rule is used toadjust said object.
 2. The method of claim 1, further comprising:obtaining change characteristics between a particular subset of two ofsaid predetermined first data with respect to one of said predeterminedsecond data, wherein said selecting includes the selecting of saidspecific second data based on the change characteristics.
 3. The methodof claim 1, wherein said selecting includes characterizing of saidsecond data group and said first data group based on said dependencytable.
 4. An adjustment rule generating apparatus which is used for anadjustment of making a first data group corresponding to outputs of apredetermined object have a desired value by determining a second datagroup corresponding to inputs to said object, the apparatus comprising:a storing device configured to store a dependency table which representsa dependency between said first data group and second data group, thedependency table being based on actual data obtained from said object; afirst selecting device configured to select, from the dependency table,predetermined first data influenced by predetermined second data, thepredetermined first data corresponding to the outputs of said object andthe predetermined second data corresponding to the inputs to saidobject; a second selecting device configured to select specific seconddata on the basis of the predetermined first data influenced by thepredetermined second data and simultaneously select specific first dataon the basis of the predetermined second data influencing thepredetermined first data; and an adjustment rule generating deviceconfigured to generate an adjustment rule by combining said specificsecond data and said specific first data, wherein said adjustment ruleis used to adjust said object.
 5. The apparatus of claim 4, wherein saidsecond selecting device selects said specific second data based on achange characteristics between a particular subset of two of saidpredetermined first data with respect to one of said predeterminedsecond data.
 6. The apparatus of claim 4, wherein said second selectingdevice comprises a characterizing device configured to characterize saidsecond data group and said first data group based on said dependencytable.
 7. An adjustment method of making a first data groupcorresponding to outputs of a predetermined object have a desired valueby determining a second data group corresponding to inputs to saidobject, the method comprising: generating an adjustment rule by:preparing a dependency table which represents a dependency between saidfirst group and said second data group, the dependency table being basedon actual data obtained from said object; obtaining, from the dependencytable, predetermined first data influenced by predetermined second data,the predetermined first data corresponding to the outputs of said objectand the predetermined second data corresponding to the inputs to saidobject; selecting specific second data on the basis of the predeterminedfirst data influenced by the predetermined second data andsimultaneously selecting specific first data on the basis of thepredetermined second data influencing the predetermined first data; andcombining said specific second data and said specific first data togenerate the adjustment rule; and adjusting said object by use of thegenerated adjustment rule.
 8. The adjustment method of claim 7, furthercomprising: repeating the generation of said adjustment rule, andstoring a plurality of adjust rules; calculating deviation between saiddesired value and a value of said first data which is output from saidobject; selecting one of said plurality of adjustment rules to beapplied to the next adjustment in relation to the deviation; anddetermining a correction amount of the second data to be adjusted inaccordance with said selected adjustment rule, as predeterminedproportional amount of the deviation.
 9. The adjustment method of claim8, further comprising: recording an adjustment history data whichindicates the previous adjustment; calculating said correction amountbased on the adjustment history data; and performing the presentadjustment by the correction amount.
 10. The adjustment method of claim9, further comprising: performing a test adjustment, thereby updatingsaid adjustment history data; and performing an actual adjustment by useof said updated adjustment history data after the test adjustment. 11.An adjustment system for making a first data group corresponding tooutputs of a predetermined object have a desired value by determining asecond data group corresponding to inputs to said object, the apparatuscomprising: a storing device configured to store a dependency tablewhich represents a dependency between said first data group and saidsecond data group, the dependency table being based on actual dataobtained from said object; an adjustment rule generating apparatusincluding: a first selecting device configured to select, from thedependency table, predetermined first data influenced by predeterminedsecond data, the predetermined first data corresponding to the outputsof said object and the predetermined second data corresponding to theinputs to said object; a second selecting device configured to selectspecific second data on the basis of the predetermined first datainfluenced by the predetermined second data and simultaneously selectspecific first data on the basis of the predetermined second datainfluencing the predetermined first data; an adjustment rule generatingdevice configured to generate an adjustment rule by combining saidspecific second data and said specific first data; and an adjustmentcontrol apparatus configured to adjust said object by use of thegenerated adjustment rule.
 12. A recording medium stored thereon acomputer readable program for enabling a computer to generate anadjustment rule used for an adjustment of making a first data groupcorresponding to outputs of a predetermined object have a desired valueby determining a second data group corresponding to inputs to saidobject, said program comprising: a first program unit for enabling thecomputer to store a dependency table which represents a dependencybetween said first data group and said second data group, the dependencytable being based on actual data obtained from said object; a secondprogram unit for enabling the computer to select, from the dependencytable, predetermined first data influenced by predetermined second data,the predetermined first data corresponding to the outputs of said objectand the predetermined second data corresponding to the inputs to saidobject; a third program unit for enabling the computer to selectspecific second data on the basis of the predetermined first datainfluenced by the predetermined second data and simultaneously selectspecific first data on the basis of the predetermined second datainfluencing the predetermined first data; and a fourth program unit forenabling the computer to generate the adjustment rule by combining saidspecific second data and said specific first data, wherein saidadjustment rule is used to adjust said object.
 13. An adjustment methodof making a first data group corresponding to outputs of a predeterminedobject have a desired value by determining a second data groupcorresponding to inputs to said object, the method comprising:obtaining, on the basis of data obtained from said object, predeterminedfirst data influenced by predetermined second data, the predeterminedfirst data corresponding to the outputs of said object and thepredetermined second data corresponding to the inputs to said object;selecting specific second data on the basis of the predetermined firstdata influenced by the predetermined second data and simultaneouslyselecting specific first data on the basis of the predetermined seconddata influencing the predetermined first data, the selecting being madebased on change characteristics obtained by analyzing changes in twoseparate ones of said outputs due to changes in only one of said inputs;and determining the specific second data capable of corresponding to theselected specific first data from the second data group, whereinselecting step performs the selecting of the specific first data on thebasis of the predetermined second data, and the selecting of thespecific second data on the basis of the predetermined second data, bydetermining dependencies between each of said inputs and each of saidoutputs, so as to create a dependency characteristic data table, and bydetermining characteristic changes of each of said inputs in relation toa particular subset of two of said outputs, for every possiblecombination of two of said outputs, so as to create a controlledvariable correlation characteristic data table, and wherein thedetermining step uses information stored in both the dependencycharacteristics data table and the controlled variable correlationcharacteristic data table to provide a set of adjustment rules for theadjustment method, wherein said set of adjustment rules are used toadjust said object.
 14. An adjustment method of making a first datagroup corresponding to outputs of a predetermined object have a desiredvalue by determining a second data group corresponding to inputs to saidobject, the method comprising: obtaining, on the basis of data obtainedfrom said object, predetermined first data influenced by predeterminedsecond data, and change characteristics between the predetermined firstdata, the predetermined first data corresponding to the outputs of saidobject and the predetermined second data corresponding to the inputs tosaid object, the change characteristics being obtained by monitoringchanges, if any, in two separate ones of the predetermined first datadue to changes in one of the predetermined second data; selectingspecific first data having change characteristics between outputscapable of correcting the change characteristics between the first datafrom the first data; and determining specific second data capable ofcorresponding to the selected specific first data from the second datagroup, wherein said second data group is inputted to said object toobtain said second data group as outputs of said object having thedesired value, to thereby